Biology subjects

Rice, B.

Publications and source records attributed to Rice, B..

10 recordsLinked to original sources

bioRxiv2026

Flywheel Genomics: Simultaneous trait discovery and genetic gain in plant breeding

Genomic mapping has yielded extensive catalogs of quantitative trait loci underlying agronomic traits, yet translating these discoveries into breeding gains remains inefficient. Here, we introduce Flywheel Genomics, a framework that integrates trait discovery directly within rapid cycling breeding populations. Using empirical data from a smallholder-oriented sorghum breeding program, we demonstrate that recurrent intermating and selection maintain genetic diversity, effective population size, and recombination while reducing confounding from plant height and maturity. Within this population, we resolve loci underlying simple adaptive and complex environmentally responsive traits and generate large segregating populations for mapping and near-isogenic lines for locus validation. We further demonstrate applicability in a public wheat breeding program, where known agronomic loci were readily detected. Simulations show that rapid cycling better preserves the population genetic properties required for Flywheel Genomics than conventional pure line development. By integrating discovery with improvement, Flywheel Genomics reframes breeding programs as engines of both crop improvement and genetic insight.

genetics↗

bioRxiv2026

Phenotyping replication is a major determinant of genomic predictive ability in sweet sorghum (Sorghum bicolor Moench)

Genomic selection can increase the rate of genetic gain in crop breeding programs, but its effectiveness depends on the reliability of phenotypic data, the size and composition of the training population (TP), and the statistical model used to estimate genomic breeding values. These design choices are especially important in resource-limited breeding programs, where additional replication, larger TPs, and more extensive genotyping compete for the same resources. Using empirical data from a sweet sorghum [Sorghum bicolor (L.) Moench] breeding population, developed by CHIBAS, we evaluated the effects of phenotyping replication, TP size, training-validation genomic relatedness, and genomic prediction (GP) model on predictive ability (PA). Grain yield, plant height, stem weight, and total soluble solids were evaluated across three field environments. Few studies in sorghum have examined these factors together with comparable empirical rigor. Increasing replication improved genomic heritability and PA for all traits and environments, with the largest gains observed for grain yield. Larger TPs and increased training-validation genomic relatedness also improved PA, but their effects were most significant when phenotype estimates were based on multiple replicates. Different GP models had comparable PA across all evaluated traits, with a few exceptions. These findings provide practical guidance for optimizing genomic selection in resource-limited sorghum breeding programs. ARTICLE SUMMARYGenomic selection can accelerate breeding only when the phenotypes used to train predictive models are reliable. Using a sweet sorghum breeding population evaluated in three Haitian field environments, we quantified how replication number, training population size, training-validation genomic relatedness, and prediction model affected genomic predictive ability for grain yield, plant height, stem weight, and total soluble solids. Replication increased genomic heritability and predictive ability for all traits, with the strongest effects for grain yield. Larger and more connected training populations improved prediction, particularly when evaluated with greater replication. These results provide practical guidance for resource-limited breeding programs. Core ideasO_LIIn this empirical sweet sorghum breeding population, phenotyping replication was the dominant factor explaining variation in genomic predictive ability across traits and environments. C_LIO_LIThe benefit of larger training populations and greater training-validation genomic relatedness increased when phenotype estimates were based on more replicates. C_LIO_LIGrain yield, the most environmentally sensitive trait evaluated, showed the largest response to improved replication and training-population design. C_LIO_LIBayesian models, rrBLUP, and GBLUP showed similar predictive abilities across traits and environments, suggesting that phenotyping and experimental design may be more important than model complexity. C_LI

genomics↗

bioRxiv2025

Computational analysis of therapeutic potential for simplified Piper. spp- derived medicinal mixtures in anxiety, sleep, pain and seizure.

Phytomedicines have played a vital role in traditional medical systems globally, particularly in providing culturally relevant and accessible healthcare solutions. Piper methysticum, known as Kava, is a traditional Pacific Island phytomedicine with clinically validated anxiolytic properties, primarily attributed to its Kavalactones. However, the biogeographically restricted distribution of Piper methysticum and the ecological and cultural concerns surrounding its widespread adoption highlight the need to explore alternative sources within the Piper genus. This study investigates whether other species within the Piper genus, used phytomedically in non-Pacific contexts, exhibit similar therapeutic efficacy for anxiety, stress, and related disorders including Post-Traumatic Stress Disorder (PTSD). We employed a computational approach utilizing a novel data platform of non-Western phytomedical pharmacopeias to analyze the secondary metabolomes of various Piper species. Network analysis and multidimensional data projections were used to compare the chemical composition and therapeutic indications of these species with those of Piper methysticum. Our findings suggest that while Kavalactones are predominantly unique to Piper methysticum, other Piper species also contain bioactive compounds associated with anxiolytic and stress-relieving effects. These results provide insight into the potential for culturally and biogeographically contextualized approaches to PTSD treatment, beyond the exclusive use of Kava, and lay the groundwork for future research into alternative phytomedicinal therapies within the Piper genus.

pharmacology and toxicology↗

bioRxiv2025

A pathway to next-generation mast cell stabilizers identified through the novel Phytomedical Analytics for Research Optimization at Scale data platform.

Mast cell stabilizers (MCS) have the potential to address unmet therapeutic need in allergy and inflammation management. MCS chronically suppress all arms of the pro-inflammatory mast cell response to stimulation (e.g., histamine, protease, lipid mediators, cytokines, chemokines). They may therefore outperform approaches such as H1, H2 and H4 inhibitors (antihistamines) which block only the acute histamine release by mast cells and leave the rest of the functional response untouched. Despite their potential, current MCS (e.g., cromolyn sodium, Tranilast, nedocromil) in clinical use are hindered by poor bioavailability, frequent dosing, long lags to onset of relief, and enigmatic mechanisms of action. MCS have their origins in phytomedicine: cromolyn sodium is the longest standing drug in the class and is a derivatized form of Khellin from Ammi visnaga, used as an anti-inflammatory. Other phytopharmacopeias may offer candidate next generation MCS (ngMCS) and in this study we hypothesized that a coupled pharmacoanalytic and in vitro pharmacology approach could be used to identify, prioritize and derisk additional candidate MCS from phytomedical sources for later pre-clinical and clinical evaluation. Here, we report a novel data analytics workflow starting with a newly developed phytopharmacopeia data platform with >3.5B linkage pathways (country [->] medical system [->] formulation [->] indication [->] ingredient organism [->] chemical component [->] other parameters), covering 22 M sq. miles of biogeography and historical and contemporary timeframes. Additional data layers include druggability indices, target and pathway analyses. The current study validates a subset of candidate phytomedical ngMCS using in silico workflow and in vitro pharmacology, and develops a new harmonic mean-based MCS score for further streamlining of the candidate prioritization process. This proof-of-concept study may have particular relevance for complex presentations such as Mast Cell Activation Syndrome (MCAS) where ngMCS may outperform current management approaches.

immunology↗

bioRxiv2024

Large-scale single-cell profiling of stem cells uncovers redundant regulators of shoot development and yield trait variation

Stem cells in plant shoots are a rare population of cells that produce leaves, fruits and seeds, vital sources for food and bioethanol. Uncovering regulators expressed in these stem cells will inform crop engineering to boost productivity. Single-cell analysis is a powerful tool for identifying regulators expressed in specific groups of cells. However, accessing plant shoot stem cells is challenging. Recent single-cell analyses of plant shoots have not captured these cells, and failed to detect stem cell regulators like CLAVATA3 and WUSCHEL. In this study, we finely dissected stem cell-enriched shoot tissues from both maize and arabidopsis for single-cell RNA-seq profiling. We optimized protocols to efficiently recover thousands of CLAVATA3 and WUSCHEL expressed cells. A cross-species comparison identified conserved stem cell regulators between maize and arabidopsis. We also performed single-cell RNA-seq on maize stem cell overproliferation mutants to find additional candidate regulators. Expression of candidate stem cell genes was validated using spatial transcriptomics, and we functionally confirmed roles in shoot development. These candidates include a family of ribosome-associated RNA-binding proteins, and two families of sugar kinase genes related to hypoxia signaling and cytokinin hormone homeostasis. These large-scale single-cell profiling of stem cells provide a resource for mining stem cell regulators, which show significant association with yield traits. Overall, our discoveries advance the understanding of shoot development and open avenues for manipulating diverse crops to enhance food and energy security.

plant biology↗

bioRxiv2023

Globally-deployed sorghum aphid resistance gene RMES1 is vulnerable to biotype shifts but being bolstered by RMES2

Durable host plant resistance (HPR) to insect pests is critical for sustainable agriculture. Natural variation exists for aphid HPR in sorghum (Sorghum bicolor) but the genetic architecture and phenotype has not been clarified for most sources. To assess the threat of a sorghum aphid (Melanaphis sorghi) biotype shift, we characterized the phenotype of Resistance to Melanaphis sorghi 1 (RMES1) and contributing HPR architecture in globally-admixed populations selected under severe aphid infestation in Haiti. We found RMES1 reduces sorghum aphid fecundity but not bird cherry-oat aphid (Rhopalosiphum padi) fecundity, suggesting a discriminant HPR response typical of gene-for-gene interaction. A second resistant gene, RMES2, were more frequent than RMES1 resistant alleles in landraces and historic breeding lines. RMES2 contributes early and mid-season aphid resistance in a segregating F2 population, however RMES1 was only significant with mid-season fitness. In a fixed population with high aphid resistance, RMES1 and RMES2 were selected for demonstrating a lack of significant antagonistic pleiotropy. Associations with resistance co-located with cyanogenic glucoside biosynthesis genes support additional HPR sources. Globally, therefore, a vulnerable HPR source (RMES1) is bolstered by a second common source of resistance in breeding programs (RMES2) which may be staving off a biotype shift. HIGHLIGHTThe globally-deployed sorghum aphid resistance gene, RMES1, reduces aphid reproduction and therefore is vulnerable to a biotype shift. A second major gene, RMES2, and cyanogenesis may increase global durability of resistance.

plant biology↗

bioRxiv2020

TNPO3-mediated nuclear entry of the Rous sarcoma virus Gag protein is independent of the cargo-binding domain

Retroviral Gag polyproteins orchestrate the assembly and release of nascent virus particles from the plasma membranes of infected cells. Although it was traditionally thought that Gag proteins trafficked directly from the cytosol to the plasma membrane, we discovered that the oncogenic avian alpharetrovirus Rous sarcoma virus (RSV) Gag protein undergoes transient nucleocytoplasmic transport as an intrinsic step in virus assembly. Using a genetic approach in yeast, we identified three karyopherins that engage the two independent nuclear localization signals (NLS) in Gag. The primary NLS is in the nucleocapsid (NC) domain of Gag and binds directly to importin-, which recruits importin-{beta} to mediate nuclear entry. The second NLS, which resides in the matrix (MA) domain, is dependent on importin-11 and transportin-3 (TNPO3), known as MTR10p and Kap120p in yeast, although it is not clear whether these import factors are independent or additive. The functionality of importin /{beta} and importin-11 has been verified in avian cells, whereas the role of TNPO3 has not been studied. In this report, we demonstrate that TNPO3 mediates nuclear entry of Gag and directly binds to Gag. To our surprise, this interaction did not require the cargo-binding domain of TNPO3, which typically mediates nuclear entry for other binding partners of TNPO3 including SR-domain containing splicing factors and tRNAs that re-enter the nucleus. These results suggest that RSV hijacks the host nuclear import pathway using a unique mechanism, potentially allowing other cargo to bind TNPO3 simultaneously. ImportanceRSV Gag nuclear entry is facilitated using three distinct host import factors that interact with nuclear localization signals in the Gag MA and NC domains. Here we show that the MA region is required for nuclear import of Gag through the TNPO3 pathway. Gag nuclear entry does not require the cargo binding domain of TNPO3. Understanding the molecular basis for TNPO3-mediated nuclear trafficking of the RSV Gag protein may lead to a deeper appreciation for whether different import factors play distinct roles in retrovirus replication.

microbiology↗

bioRxiv2020

RNA binding domains of heterologous viral proteins substituted for basic residues in the RSV Gag NC domain restore specific packaging of genomic RNA

The Rous sarcoma virus Gag polyprotein transiently traffics through the nucleus, which is required for efficient incorporation of the viral genomic RNA (gRNA) into virus particles. Packaging of gRNA is mediated by two zinc knuckles and basic residues located in the nucleocapsid (NC) domain in Gag. To further examine the role of basic residues located downstream of the zinc knuckles in gRNA encapsidation, we used a gain-of-function approach. We replaced a basic residue cluster essential for gRNA packaging with heterologous basic residue motif (BR) with RNA binding activity from either the HIV-1 Rev protein (Rev BR) or the HSV ICP27 protein (ICP27 BR). Compared to wild type Gag, the mutant ICP27 BR and Rev BR Gag proteins were much more strongly localized to the nucleus and released significantly lower levels of virus particles. Surprisingly, both the ICP27 BR and Rev BR mutants packaged normal levels of gRNA per virus particle when examined in the context of a proviral vector, yet both mutants were noninfectious. These results support the hypothesis that basic residues located in the C-terminal region of NC are required for selective gRNA packaging, potentially via initial electrostatic interactions that facilitate specific binding.

microbiology↗

bioRxiv2018

Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA

BackgroundBlood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumor-derived cfDNA has proven challenging because of the small proportion of cfDNA derived from tumor tissue in early-stage disease. A machine learning approach to discover signatures in cfDNA, potentially reflective of both tumor and non-tumor contributions, may represent a promising direction for the early detection of cancer.\n\nMethodsWhole-genome sequencing was performed on cfDNA extracted from plasma samples (N=546 colorectal cancer and 271 non-cancer controls). Reads aligning to protein-coding gene bodies were extracted, and read counts were normalized. cfDNA tumor fraction was estimated using IchorCNA. Machine learning models were trained using k-fold cross-validation and confounder-based cross-validation to assess generalization performance.\n\nResultsIn a colorectal cancer cohort heavily weighted towards early-stage cancer (80% stage I/II), we achieved a mean AUC of 0.92 (95% CI 0.91-0.93) with a mean sensitivity of 85% (95% CI 83-86%) at 85% specificity. Sensitivity generally increased with tumor stage and increasing tumor fraction. Stratification by age, sequencing batch, and institution demonstrated the impact of these confounders and provided a more accurate assessment of generalization performance.\n\nConclusionsA machine learning approach using cfDNA achieved high sensitivity and specificity in a large, predominantly early-stage, colorectal cancer cohort. The possibility of systematic technical and institution-specific biases warrants similar confounder analyses in other studies. Prospective validation of this machine learning method and evaluation of a multi-analyte approach are underway.

cancer biology↗

bioRxiv2018

Structural variation detection by proximity ligation from FFPE tumor tissue

The clinical management and therapy of many solid tumor malignancies is dependent on detection of medically actionable or diagnostically relevant genetic variation. However, a principal challenge for genetic assays from tumors is the fragmented and chemically damaged state of DNA in formalin-fixed paraffin-embedded (FFPE) samples. From highly fragmented DNA and RNA there is no current technology for generating long-range DNA sequence data as is required to detect genomic structural variation or long-range genotype phasing. We have developed a high-throughput chromosome conformation capture approach for FFPE samples that we call \"Fix-C\", which is similar in concept to Hi-C. Fix-C enables structural variation detection from fresh and archival FFPE samples. We applied this method to 15 clinical adenocarcinoma and sarcoma specimens spanning a broad range of tumor purities. In this panel, Fix-C analysis achieves a 90% concordance rate with FISH assays - the current clinical gold standard. Additionally, we are able to identify novel structural variation undetected by other methods and recover long-range chromatin configuration information from these FFPE samples harboring highly degraded DNA. This powerful approach will enable detailed resolution of global genome rearrangement events during cancer progression from FFPE material, and inform the development of targeted molecular diagnostic assays for patient care.

genomics↗