Spheroid architecture strongly enhances miR-221/222 expression and promotes oxidative phosphorylation in an ovarian cancer cell line through a mechanism that includes restriction of miR-9 expression.

BACKGROUND: Tumor cell spheroids are organized multicellular structures that form during the expansive growth of carcinoma cells. Spheroids formation is thought to contribute to metastasis by supporting growth and survival of mobile tumor cell populations. METHODS AND RESULTS: We investigated how spheroid architecture affects OXPHOS activity, microRNA expression, and intraperitoneal survival of an ovarian carcinoma cell line using high resolution respirometry, quantitative RT-PCR, and a rodent intraperitoneal growth model. Rates of oxidative phosphorylation/respiration per cell of cells growing as spheroids were nearly double those of a variant of the same cell type growing in suspension as loosely aggregated cells. Further, inhibition of spheroid formation by treatment with CDH2 (N-cadherin) siRNA reduced the rate of OXPHOS to that of the non-spheroid forming variant. Cells growing as spheroids showed greatly enhanced expression of miR-221/222, an oncomiR that targets multiple tumor suppressor genes and promotes invasion, and reduced expression of miR-9, which targets mitochondrial tRNA-modification enzymes and inhibits OXPHOS. Consistent with greater efficiency of ATP generation, tumor cells growing as spheroids injected into the nutrient-poor murine peritoneum survived longer than cells growing in suspension as loosely associated aggregates. CONCLUSIONS: The data indicate that growth in spheroid form enhances the OXPHOS activity of constituent tumor cells. In addition, spheroid architecture affects expression of microRNA genes involved in growth control and mitochondrial function. During the mobile phase of metastasis, when ovarian tumor cells disperse through nutrient-poor environments such as the peritoneum, enhanced OXPHOS activity afforded by spheroid architecture would enhance survival and metastatic potential.

Multi-trait genomic selection improves the prediction accuracy of end-use quality traits in hard winter wheat.

Improvement of end-use quality remains one of the most important goals in hard winter wheat (HWW) breeding. Nevertheless, the evaluation of end-use quality traits is confined to later development generations owing to resource-intensive phenotyping. Genomic selection (GS) has shown promise in facilitating selection for end-use quality; however, lower prediction accuracy (PA) for complex traits remains a challenge in GS implementation. Multi-trait genomic prediction (MTGP) models can improve PA for complex traits by incorporating information on correlated secondary traits, but these models remain to be optimized in HWW. A set of advanced breeding lines from 2015 to 2021 were genotyped with 8725 single-nucleotide polymorphisms and was used to evaluate MTGP to predict various end-use quality traits that are otherwise difficult to phenotype in earlier generations. The MTGP model outperformed the ST model with up to a twofold increase in PA. For instance, PA was improved from 0.38 to 0.75 for bake absorption and from 0.32 to 0.52 for loaf volume. Further, we compared MTGP models by including different combinations of easy-to-score traits as covariates to predict end-use quality traits. Incorporation of simple traits, such as flour protein (FLRPRO) and sedimentation weight value (FLRSDS), substantially improved the PA of MT models. Thus, the rapid low-cost measurement of traits like FLRPRO and FLRSDS can facilitate the use of GP to predict mixograph and baking traits in earlier generations and provide breeders an opportunity for selection on end-use quality traits by culling inferior lines to increase selection accuracy and genetic gains.

ETV6 Deficiency Unlocks ERG-Dependent Microsatellite Enhancers to Drive Aberrant Gene Activation in B-Lymphoblastic Leukemia.

Distal enhancers play critical roles in sustaining oncogenic gene-expression programs. We identify aberrant enhancer-like activation of GGAA tandem repeats as a characteristic feature of B-cell acute lymphoblastic leukemia (B-ALL) with genetic defects of the ETV6 transcriptional repressor, including ETV6-RUNX1+ and ETV6-null B-ALL. We show that GGAA repeat enhancers are direct activators of previously identified ETV6-RUNX1+/- like B-ALL "signature" genes, including the likely leukemogenic driver EPOR. When restored to ETV6-deficient B-ALL cells, ETV6 directly binds to GGAA repeat enhancers, represses their acetylation, downregulates adjacent genes, and inhibits B-ALL growth. In ETV6-deficient B-ALL cells, we find that the ETS transcription factor ERG directly binds to GGAA microsatellite enhancers and is required for sustained activation of repeat enhancer-activated genes. Together, our findings reveal an epigenetic gatekeeper function of the ETV6 tumor suppressor gene and establish microsatellite enhancers as a key mechanism underlying the unique gene-expression program of ETV6-RUNX1+/- like B-ALL. SIGNIFICANCE: We find a unifying mechanism underlying a leukemia subtype-defining gene-expression signature that relies on repetitive elements with poor conservation between humans and rodents. The ability of ETV6 to antagonize promiscuous, nonphysiologic ERG activity may shed light on other roles of these key regulators in hematolymphoid development and human disease. See related commentary by Mercher, p. 2. This article is highlighted in the In This Issue feature, p. 1.

Selective Enhancer Dependencies in MYC -Intact and MYC -Rearranged Germinal Center B-cell Diffuse Large B-cell Lymphoma.

High expression of MYC and its target genes define a subset of germinal center B-cell diffuse large B-cell lymphoma (GCB-DLBCL) associated with poor outcomes. Half of these high-grade cases show chromosomal rearrangements between the MYC locus and heterologous enhancer-bearing loci, while focal deletions of the adjacent non-coding gene PVT1 are enriched in MYC -intact cases. To identify genomic drivers of MYC activation, we used high-throughput CRISPR-interference (CRISPRi) profiling of candidate enhancers in the MYC locus and rearrangement partner loci in GCB-DLBCL cell lines and mantle cell lymphoma (MCL) comparators that lacked common rearrangements between MYC and immunoglobulin (Ig) loci. Rearrangements between MYC and non-Ig loci were associated with unique dependencies on specific enhancer subunits within those partner loci. Notably, fitness dependency on enhancer modules within the BCL6 super-enhancer ( BCL6 -SE) cluster regulated by a transcription factor complex of MEF2B, POU2F2, and POU2AF1 was higher in cell lines bearing a recurrent MYC::BCL6 -SE rearrangement. In contrast, GCB-DLBCL cell lines without MYC rearrangement were highly dependent on a previously uncharacterized 3' enhancer within the MYC locus itself (GCBME-1), that is regulated in part by the same triad of factors. GCBME-1 is evolutionarily conserved and active in normal germinal center B cells in humans and mice, suggesting a key role in normal germinal center B cell biology. Finally, we show that the PVT1 promoter limits MYC activation by either native or heterologous enhancers and demonstrate that this limitation is bypassed by 3' rearrangements that remove PVT1 from its position in cis with the rearranged MYC gene. Key points: CRISPR-interference screens identify a conserved germinal center B cell MYC enhancer that is essential for GCB-DLBCL lacking MYC rearrangements. Functional profiling of MYC partner loci reveals principles of MYC enhancer-hijacking activation by non-immunoglobulin rearrangements.

EWS::FLI1 and HOXD13 Control Tumor Cell Plasticity in Ewing Sarcoma.

PURPOSE: Propagation of Ewing sarcoma requires precise regulation of EWS::FLI1 transcriptional activity. Determining the mechanisms of fusion regulation will advance our understanding of tumor progression. Here we investigated whether HOXD13, a developmental transcription factor that promotes Ewing sarcoma metastatic phenotypes, influences EWS::FLI1 transcriptional activity. EXPERIMENTAL DESIGN: Existing tumor and cell line datasets were used to define EWS::FLI1 binding sites and transcriptional targets. Chromatin immunoprecipitation and CRISPR interference were employed to identify enhancers. CUT&RUN and RNA sequencing defined binding sites and transcriptional targets of HOXD13. Transcriptional states were investigated using bulk and single-cell transcriptomic data from cell lines, patient-derived xenografts, and patient tumors. Mesenchymal phenotypes were assessed by gene set enrichment, flow cytometry, and migration assays. RESULTS: We found that EWS::FLI1 creates a de novo GGAA microsatellite enhancer in a developmentally conserved regulatory region of the HOXD locus. Knockdown of HOXD13 led to widespread changes in expression of developmental gene programs and EWS::FLI1 targets. HOXD13 binding was enriched at established EWS::FLI1 binding sites where it influenced expression of EWS::FLI1-activated genes. More strikingly, HOXD13 bound and activated EWS::FLI1-repressed genes, leading to adoption of mesenchymal and migratory cell states that are normally suppressed by the fusion. Single-cell analysis confirmed that direct transcriptional antagonism between HOXD13-mediated gene activation and EWS::FLI1-dependent gene repression defines the state of Ewing sarcoma cells along a mesenchymal axis. CONCLUSIONS: Ewing sarcoma tumors are comprised of tumor cells that exist along a mesenchymal transcriptional continuum. The identity of cells along this continuum is, in large part, determined by the competing activities of EWS::FLI1 and HOXD13. See related commentary by Weiss and Bailey, p. 4360.

Multi-Trait Multi-Environment Genomic Prediction of Agronomic Traits in Advanced Breeding Lines of Winter Wheat.

Genomic prediction is a promising approach for accelerating the genetic gain of complex traits in wheat breeding. However, increasing the prediction accuracy (PA) of genomic prediction (GP) models remains a challenge in the successful implementation of this approach. Multivariate models have shown promise when evaluated using diverse panels of unrelated accessions; however, limited information is available on their performance in advanced breeding trials. Here, we used multivariate GP models to predict multiple agronomic traits using 314 advanced and elite breeding lines of winter wheat evaluated in 10 site-year environments. We evaluated a multi-trait (MT) model with two cross-validation schemes representing different breeding scenarios (CV1, prediction of completely unphenotyped lines; and CV2, prediction of partially phenotyped lines for correlated traits). Moreover, extensive data from multi-environment trials (METs) were used to cross-validate a Bayesian multi-trait multi-environment (MTME) model that integrates the analysis of multiple-traits, such as G × E interaction. The MT-CV2 model outperformed all the other models for predicting grain yield with significant improvement in PA over the single-trait (ST-CV1) model. The MTME model performed better for all traits, with average improvement over the ST-CV1 reaching up to 19, 71, 17, 48, and 51% for grain yield, grain protein content, test weight, plant height, and days to heading, respectively. Overall, the empirical analyses elucidate the potential of both the MT-CV2 and MTME models when advanced breeding lines are used as a training population to predict related preliminary breeding lines. Further, we evaluated the practical application of the MTME model in the breeding program to reduce phenotyping cost using a sparse testing design. This showed that complementing METs with GP can substantially enhance resource efficiency. Our results demonstrate that multivariate GS models have a great potential in implementing GS in breeding programs.

Towards novel herbicide modes of action by inhibiting lysine biosynthesis in plants.

Weeds are becoming increasingly resistant to our current herbicides, posing a significant threat to agricultural production. Therefore, new herbicides with novel modes of action are urgently needed. In this study, we exploited a novel herbicide target, dihydrodipicolinate synthase (DHDPS), which catalyses the first and rate-limiting step in lysine biosynthesis. The first class of plant DHDPS inhibitors with micromolar potency against Arabidopsis thaliana DHDPS was identified using a high-throughput chemical screen. We determined that this class of inhibitors binds to a novel and unexplored pocket within DHDPS, which is highly conserved across plant species. The inhibitors also attenuated the germination and growth of A. thaliana seedlings and confirmed their pre-emergence herbicidal activity in soil-grown plants. These results provide proof-of-concept that lysine biosynthesis represents a promising target for the development of herbicides with a novel mode of action to tackle the global rise of herbicide-resistant weeds.

Differential lysine-mediated allosteric regulation of plant dihydrodipicolinate synthase isoforms.

Lysine biosynthesis in plants occurs via the diaminopimelate pathway. The first committed and rate-limiting step of this pathway is catalysed by dihydrodipicolinate synthase (DHDPS), which is allosterically regulated by the end product, l-lysine (lysine). Given that lysine is a common nutritionally limiting amino acid in cereal crops, there has been much interest in probing the regulation of DHDPS. Interestingly, knockouts in Arabidopsis thaliana of each isoform (AtDHDPS1 and AtDHDPS2) result in different phenotypes, despite the enzymes sharing > 85% protein sequence identity. Accordingly, in this study, we compared the catalytic activity, lysine-mediated inhibition and structures of both A. thaliana DHDPS isoforms. We found that although the recombinantly produced enzymes have similar kinetic properties, AtDHDPS1 is 10-fold more sensitive to lysine. We subsequently used X-ray crystallography to probe for structural differences between the apo- and lysine-bound isoforms that could account for the differential allosteric inhibition. Despite no significant changes in the overall structures of the active or allosteric sites, we noted differences in the rotamer conformation of a key allosteric site residue (Trp116) and proposed that this could result in differences in lysine dissociation. Microscale thermophoresis studies supported our hypothesis, with AtDHDPS1 having a ~ 6-fold tighter lysine dissociation constant compared to AtDHDPS2, which agrees with the lower half minimal inhibitory concentration for lysine observed. Thus, we highlight that subtle differences in protein structures, which could not have been predicted from the primary sequences, can have profound effects on the allostery of a key enzyme involved in lysine biosynthesis in plants. DATABASES: Structures described are available in the Protein Data Bank under the accession numbers 6VVH and 6VVI.

Review: amino acid biosynthesis as a target for herbicide development.

There are three amino acid biosynthesis pathways that are targeted by current herbicides, namely those leading to the production of aromatic amino acids, branched chain amino acids and glutamine. However, their efficacy is diminishing as a result of the increasing number of resistant weeds. Indeed, resistance to most classes of herbicides is on the rise, posing a significant threat to the utility of current herbicides to sustain effective weed management. This review provides an overview of potential herbicide targets within amino acid biosynthesis that remain unexploited commercially, and recent inhibitor discovery efforts. Despite contemporary approaches to herbicide discovery, such as chemical repurposing and the use of omics technologies, there have been no new products introduced to the market that inhibit amino acid biosynthesis over the past three decades. This highlights the chasm that exists between identifying a potent inhibitor and introducing a commercial herbicide. The unpredictability of a mode of action at the systemic level, as well as poor physicochemical properties, often contribute to a lack of progression beyond the target inhibition stage. Nevertheless, it will be important to overcome these obstacles for the development of new herbicides to protect our agricultural industry and ensure food security for an increasing world population. © 2020 Society of Chemical Industry.

2019 John M. Eisenberg Patient Safety and Quality Awards: SPOTting Sepsis to Save Lives: A Nationwide Computer Algorithm for Early Detection of Sepsis: Innovation in Patient Safety and Quality at the National Level (Eisenberg Award).

BACKGROUND: In recognition of the potential of data to drive care and the need for early identification of patients with sepsis, HCA Healthcare developed an automated sepsis detection algorithm-SPOT (Sepsis Prediction and Optimization of Therapy). The algorithm was deployed at scale and served as a mechanism to reduce the time to detection and improve sepsis mortality in 173 hospitals across the United States. METHODS: The SPOT algorithm was designed as a rules-based detection of defined criteria that would interpret available electronic and basic laboratory data in near real time. Working from an organizational recognition of the need to construct a national clinical data warehouse to allow for the aggregation and analysis of data streams, HCA Healthcare designed and deployed SPOT and delivered the alert from the algorithm to the bedside to initiate existing clinical workflows for patients with sepsis. RESULTS: SPOT improved the timeliness of sepsis detection by providing alerts when signals of sepsis become available, triggering initiation of sepsis screens. This gave an advantage of about six hours over the legacy practice of sepsis screening at shift change. When deployed alongside existing sepsis improvement initiatives, SPOT was associated with an acceleration of improvement in mortality-particularly in the not-present-on-admission (NPOA) septic shock population, the patients at greatest risk for mortality. This population had seen little improvement with prior initiatives, but mortality improved 3.9 percentage points from 2018 to 2019. When accounting for seasonal variation, there was a decline in mortality rate following the deployment of SPOT, as compared to the year prior, of 9.9% for NPOA severe sepsis and 5.1% for NPOA septic shock. CONCLUSION: Development of the SPOT algorithm for the detection of sepsis from data available in the electronic health record resulted in more timely recognition, faster initiation of treatment, and improved survival for patients.

Identification of two dihydrodipicolinate synthase isoforms from Pseudomonas aeruginosa that differ in allosteric regulation.

Pseudomonas aeruginosa is one of the leading causes of nosocomial infections, accounting for 10% of all hospital-acquired infections. Current antibiotics against P. aeruginosa are becoming increasingly ineffective due to the exponential rise in drug resistance. Thus, there is an urgent need to validate and characterize novel drug targets to guide the development of new classes of antibiotics against this pathogen. One such target is the diaminopimelate (DAP) pathway, which is responsible for the biosynthesis of bacterial cell wall and protein building blocks, namely meso-DAP and lysine. The rate-limiting step of this pathway is catalysed by the enzyme dihydrodipicolinate synthase (DHDPS), typically encoded for in bacteria by a single dapA gene. Here, we show that P. aeruginosa encodes two functional DHDPS enzymes, PaDHDPS1 and PaDHDPS2. Although these isoforms have similar catalytic activities (kcat  = 29 s-1 and 44 s-1 for PaDHDPS1 and PaDHDPS2, respectively), they are differentially allosterically regulated by lysine, with only PaDHDPS2 showing inhibition by the end product of the DAP pathway (IC50  = 130 µm). The differences in allostery are attributed to a single amino acid difference in the allosteric binding pocket at position 56. This is the first example of a bacterium that contains multiple bona fide DHDPS enzymes, which differ in allosteric regulation. We speculate that the presence of the two isoforms allows an increase in the metabolic flux through the DAP pathway when required in this clinically important pathogen. DATABASES: PDB ID: 6P90.