Biology subjects

Griffiths, M.

Publications and source records attributed to Griffiths, M..

14 recordsLinked to original sources

bioRxiv2026

Molecular dynamics of Brodmann Area 22 in development and autism

Challenges in verbal communication are a prominent feature of autism. However, gene regulatory programs in speech-related cortical regions remain poorly characterized. In parallel, it remains unclear whether the heterogeneous genetic factors underlying autism converge on shared neurobiological mechanisms. To address these gaps, we generated paired transcriptomic and epigenomic data from post-mortem human brain tissue across 100 donors. Here, we show that transcriptional differences in the speech-related Brodmann Area 22 in individuals with neurodevelopmental conditions, including autism, are strongest among those with a known genetic diagnosis. A similar but attenuated signature is observed in those without a genetic diagnosis. These transcriptional differences are most pronounced in neurons, with glutamatergic L4/5 intratelencephalic neurons affected across multiple modalities. Finally, multimodal analysis implicates altered RFX3-dependent networks as a central hub in autism, particularly among L4/5 intratelencephalic neurons in non-verbal individuals. Together, our study identifies regulatory architecture linking chromatin state, transcriptional output, and variation in verbal ability in autism.

neuroscience↗

bioRxiv2026

KyDab - a comprehensive database of antibody discovery selection campaigns.

Preclinical antibody discovery relies on progressive screening and down-selection of candidate antibodies from large immune repertoires, yet this critical process is poorly represented in existing public databases. Here we introduce KyDab (Kymouse Antibody Database), a well-curated database of antibody discovery selection data generated using standardized workflows on the Kymouse humanized mouse platform. The current release includes 11 Kymouse platform mice immunisation studies covering 51 immunogens, more than 120,000 paired heavy-light chain sequences, and binding measurements for a selected subset of experimentally characterized clones. By capturing full-funnel selection data with consistent metadata and both positive and negative experimental outcomes, KyDab provides a valuable data resource for the development and evaluation of artificial intelligence models for antibody discovery. KyDab is accessible at https://kydab.naturalantibody.com/, and the database will be continuously updated as new datasets become available.

bioinformatics↗

bioRxiv2025

Disruption of Cell-Type-Specific Molecular Programs of Medium Spiny Neurons in Autism

Autism spectrum disorders (ASD) are highly heritable neurodevelopmental conditions with major contributions from rare genetic variants. Most studies have focused on cortical mechanisms; even growing evidence implicates subcortical circuits in ASD etiology. To systematically map developmental and molecular alterations beyond the cortex, we profiled lineage relationships across five brain regions in an ASD mouse model. Most prominent changes emerged in the striatum, a hub for learning and motor control. Furthermore, we performed single-nucleus multiomic profiling of human putamen from ASD and neurotypical donors revealed cell-type-specific transcriptomic and regulatory alterations. Differential expression converged on synaptic and energy metabolic dysfunctions in D1 striosome medium spiny neurons (MSNs), coupled with astrocytic remodeling of synaptic support. Gene regulatory network analysis identified EGR3 and EGR1 as key transcriptional regulators of ASD-associated programs of D1 MSNs. Together, these results establish the striatum as a central node of ASD convergence and provide a multiomic resource for dissecting its subcortical mechanisms.

neuroscience↗

bioRxiv2025

Population and adaptation history of 739 Thlaspi arvense natural accessions

Pennycress (Thlaspi arvense) is a promising intermediate oilseed crop, producing oil suitable for conversion to biofuels--including aviation fuels. While domestication efforts are ongoing, a deeper understanding of the genetic architecture of traits is crucial for informing future breeding efforts. Here, we conducted the largest genomic and phenotypic survey of pennycress to date, analyzing 739 accessions collected across four continents. Leveraging whole-genome sequencing and field-collected phenotypes, we characterized the standing genetic variation underlying key agronomic traits and climate resilience. Our findings revealed multiple independent migration events to North America, with substantial genetic admixture. We identified homologs of Arabidopsis thaliana flowering-time genes that contribute to adaptation and demonstrated the agronomic benefits of winter-type pennycress. Furthermore, through multi-season field trials, we identified a genomic region containing a cluster of mTERF genes strongly associated with green canopy coverage, a critical trait for biomass retention and yield stability. These insights provide a genomic roadmap for accelerating pennycress domestication and improving its resilience to climate variability.

genomics↗

bioRxiv2025

Loss of PIF7 attenuates shade and elevated temperature responses throughout the lifecycle in Pennycress

Pennycress (Thlaspi arvense) is being developed as a winter annual intermediate oilseed bioenergy crop in the Midwest during typical fallow periods. Crucial work remains to domesticate and optimize pennycress for incorporation into cropping systems and increasing resilience to rising temperatures. We found that increased planting density reduces biomass and hastens time to flowering and maturity, which are associated with shade avoidance responses. In controlled conditions, we found that pennycress elongates in response to foliar shade and increased ambient temperatures (28 {degrees}C). We applied the knowledge base from Arabidopsis thaliana to manipulate genes in the PHYB signaling pathway to simultaneously decrease the shade avoidance response during interseeding and tissue responses to elevated temperatures. Evaluation of CRISPR alleles of PIF7 shows that pif7 reduces organ elongation to competition and heat cues and retains a compact rosette when exposed to shade or elevated temperature and their combination. Crucially, yield and oil content were unaltered in pif7 and plants maintained earlier flowering in stress conditions. Furthermore, indicators of plant health, such as hue, chlorophyll indices, and root system architecture, were improved between wild type and pif7. This is evidence that plant architecture and physiological health can be uncoupled under competition and heat conditions, supporting our efforts to attenuate morphological responses to environmental cues. We propose this strategy for reducing SAR, improving pennycress performance at high densities, for during interseeding establishment in standing crops, and in a warming climate.

plant biology↗

bioRxiv2023

A temporal atlas and response to nitrate availability of 3D root system architecture in diverse pennycress (Thlaspi arvense L.) accessions

1Roots have a central role in plant resource capture and are the interface between the plant and the soil that affect multiple ecosystem processes. Field pennycress (Thlaspi arvense L.) is a diploid annual cover crop species that has potential utility for reducing soil erosion and nutrient losses; and has rich seeds (30-35% oil) amenable to biofuel production and as a protein animal feed. The objective of this research was to (1) precisely characterize root system architecture and development, (2) understand adaptive responses of pennycress roots to nitrate nutrition, (3) and determine genotypic variance available in root development and nitrate plasticity. Using a root imaging and analysis pipeline, 4D pennycress root system architecture was characterized under four nitrate regimes (from zero to high nitrate concentration) across four time points (days 5, 9, 13, and 17 after sowing). Significant nitrate condition response and genotype interactions were identified for many root traits with greatest impact on lateral root traits. In trace nitrate conditions a greater lateral root count, length, interbranch density, and a steeper lateral root angle was observed compared to high nitrate conditions. Genotype-by-nitrate condition interaction was observed for root width, width:depth ratio, mean lateral root length, and lateral root density. These results illustrate root trait variance available in pennycress accessions that could be useful targets for breeding of improved nitrate responsive cover crops for greater productivity, resilience, and ecosystem service.

plant biology↗

bioRxiv2021

Application of synthetic peptide CEP1 increases nutrient uptake rates along plant roots

The root system of a plant provides vital functions including resource uptake, storage, and anchorage in soil. Uptake from the soil of macro-nutrients like nitrogen (N), phosphorus (P), potassium (K), and sulphur (S) is critical for plant growth and development. Small signaling peptide (SSP) hormones are best known as potent regulators of plant growth and development with a few also known to have specialized roles in macronutrient utilization. Here we describe a high-throughput screen of SSP effects on root uptake of multiple nutrients. The SSP, MtCEP1 enhanced nitrate uptake rate per unit root length in Medicago truncatula plants deprived of N. MtCEP1 and AtCEP1 enhanced uptake not only of nitrate, but also phosphate and sulfate in both Medicago and Arabidopsis. Transcriptome analysis of Medicago roots treated with different MtCEP1 encoded peptide domains revealed that hundreds of genes respond to these peptides, including several nitrate transporters and a sulfate transporter that may mediate the uptake of these macronutrients downstream of CEP1 signaling. Likewise, several putative signaling pathway genes were induced in roots by CEP1 treatment. Thus, a scalable method has been developed for screening synthetic peptides of potential use in agriculture, with CEP1 shown to be one such peptide.

plant biology↗

bioRxiv2021

RhizoVision Explorer: Open-source software for root image analysis and measurement standardization

Roots are central to the function of natural and agricultural ecosystems by driving plant acquisition of soil resources and influencing the carbon cycle. Root characteristics like length, diameter, and volume are critical to measure to understand plant and soil functions. RhizoVision Explorer is an open-source software designed to enable researchers interested in roots by providing an easy-to-use interface, fast image processing, and reliable measurements. The default broken roots mode is intended for roots sampled from pots or soil cores, washed, and typically scanned on a flatbed scanner, and provides measurements like length, diameter, and volume. The optional whole root mode for complete root systems or root crowns provides additional measurements such as angles, root depth, and convex hull. Both modes support providing measurements grouped by defined diameter ranges, the inclusion of multiple regions of interest, and batch analysis. RhizoVision Explorer was successfully validated against ground truth data using a novel copper wire image set. In comparison, the current reference software, the commercial WinRhizo, drastically underestimated volume when wires of different diameters were in the same image. Additionally, measurements were compared with WinRhizo and IJ_Rhizo using a simulated root image set, showing general agreement in software measurements, except for root volume. Finally, scanned root image sets acquired in different labs for the crop, herbaceous, and tree species were used to compare results from RhizoVision Explorer with WinRhizo. The two software showed general agreement, except that WinRhizo substantially underestimated root volume relative to RhizoVision Explorer. In the current context of rapidly growing interest in root science, RhizoVision Explorer intends to become a reference software, improve the overall accuracy and replicability of root trait measurements, and provide a foundation for collaborative improvement and reliable access to all. O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

plant biology↗

bioRxiv2021

Interactions among rooting traits for deep water and nitrogen uptake in upland and lowland ecotypes of switchgrass (Panicum virgatum L.)

Plant phenotypic plasticity in response to nutrient and water availability is an important adaptation for abiotic stress tolerance. Roots intercept water and nutrients while foraging through soil searching for further resources. Substantial amounts of nitrate can leach into groundwater; yet, little is known about how deep rooting affects this process. Here, we phenotyped root system traits and deep 15N nitrate capture across 1.5 m profiles of solid-media using tall mesocosms in switchgrass (Panicum virgatum L.), a cellulosic bioenergy feedstock. Root and shoot biomass, photosynthesis and respiration, and nutrient uptake traits were quantified in response to a water and nitrate stress factorial experiment in the greenhouse for switchgrass upland (VS16) and lowland (AP13) ecotypes. The two switchgrass ecotypes shared common plastic abiotic responses to nitrogen (N) and water availability and yet showed genotypic differences for root and shoot traits. A significant interaction between nitrogen and water stress for axial and lateral root traits represents a complex and shared root development strategy for stress mitigation. Deep root growth and 15N capture were found to be closely linked to aboveground growth. Together, these results represent the wide genetic pool of switchgrass and that deep rooting promotes nitrate capture, plant productivity, and sustainability. HighlightTwo main ecotypes of switchgrass have both shared and different root responses to varying water and nitrogen conditions, with deep rooting shown to be closely linked to aboveground growth.

plant biology↗

bioRxiv2020

A systematic evaluation of IgM and IgG antibody assay accuracy in diagnosing acute Zika Virus infection in Brazil; lessons relevant to emerging infections

Accurate diagnostics underpin effective public health responses to emerging viruses. For viruses, such as Zika virus (ZIKV), where the viremia clears quickly, antibody-based (IgM or IgG) diagnostics are recommended for patients who present seven days after symptom onset. However, cross-reactive antibody responses can complicate test interpretation among populations where closely related viruses circulate. We examined the accuracy (proportion of samples correctly categorized as Zika-positive or negative) for antibody-based diagnostics among Brazilian residents (Rio de Janeiro) during the ZIKV outbreak. Four ZIKV ELISAs (IgM and IgG Euroimmun, IgM Novagnost and CDC MAC), two dengue ELISAs (IgM and IgG Panbio), and the ZIKV plaque reduction neutralization test (PRNT) were evaluated. Positive samples were ZIKV PCR confirmed clinical cases collected in 2015-2016 (n=169); Negative samples (n=236) were collected before ZIKV was present in Brazil ([≤]2013). Among serum samples collected [≥]7 days from symptom onset, PRNT exhibited the highest accuracy (93.7%), followed by the Euroimmun IgG ELISA (77.9%). All IgM assays exhibited lower accuracy (<74%). IgG was detected more consistently than IgM among ZIKV cases using Euroimmun ELISAs (68% versus 22%). Anti-DENV IgM ELISA was positive in 41.1% of confirmed ZIKV samples tested. The Euroimmun IgG assay, although misdiagnosing 22% of samples, provided the most accurate ELISA. Anti-ZIKV IgG was detected more reliably than IgM among ZIKV patients, suggesting a secondary antibody response to assay antigens following ZIKV infection. Antibody ELISAs need careful evaluation in their target population to optimise use and minimise misdiagnosis, prior to widespread deployment, particularly where related viruses co-circulate.

microbiology↗

bioRxiv2020

Functional phenomics and genetics of the root economics space in winter wheat using high-throughput phenotyping of respiration and architecture

O_LIThe root economics space is a useful framework for plant ecology, but rarely considered for crop ecophysiology. In order to understand root trait integration in winter wheat, we combined functional phenomics with trait economic theory utilizing genetic variation, high-throughput phenotyping, and multivariate analyses. C_LIO_LIWe phenotyped a diversity panel of 276 genotypes for root respiration and architectural traits using a novel high-throughput method for CO2 flux and the open-source software RhizoVision Explorer for analyzing scanned images. C_LIO_LIWe uncovered substantial variation for specific root respiration (SRR) and specific root length (SRL), which were primary indicators of root metabolic and construction costs. Multiple linear regression estimated that lateral root tips had the greatest SRR, and the residuals of this model were used as a new trait. SRR was negatively correlated with plant mass. Network analysis using a Gaussian graphical model identified root weight, SRL, diameter, and SRR as hub traits. Univariate and multivariate genetic analyses identified genetic regions associated with aspects of the root economics space, with underlying gene candidates. C_LIO_LICombining functional phenomics and root economics is a promising approach to understand crop ecophysiology. We identified root traits and genomic regions that could be harnessed to breed more efficient crops for sustainable agroecosystems. C_LI

plant biology↗

bioRxiv2019

High-throughput phenotyping reveals expansive genetic and structural underpinnings of immune variation

By developing a high-density murine immunophenotyping platform compatible with high-throughput genetic screening, we have established profound contributions of genetics and structure to immune variation. Specifically, high-throughput phenotyping of 530 knockout mouse lines identified 140 monogenic \"hits\" (>25%), most of which had never hitherto been implicated in immunology. Furthermore, they were conspicuously enriched in genes for which humans show poor tolerance to loss-of-function. The immunophenotyping platform also exposed dense correlation networks linking immune parameters with one another and with specific physiologic traits. By limiting the freedom of individual immune parameters, such linkages impose genetically regulated \"immunological structures\", whose integrity was found to be associated with immunocompetence. Hence, our findings provide an expanded genetic resource and structural perspective for understanding and monitoring immune variation in health and disease.

immunology↗

bioRxiv2019

RhizoVision Crown: An Integrated Hardware and Software Platform for Root Crown Phenotyping

Root crown phenotyping measures the top portion of crop root systems and can be used for marker-assisted breeding, genetic mapping, and understanding how roots influence soil resource acquisition. Several imaging protocols and image analysis programs exist, but they are not optimized for high-throughput, repeatable, and robust root crown phenotyping. The RhizoVision Crown platform integrates an imaging unit, image capture software, and image analysis software that are optimized for reliable extraction of measurements from large numbers of root crowns. The hardware platform utilizes a back light and a monochrome machine vision camera to capture root crown silhouettes. RhizoVision Imager and RhizoVision Analyzer are free, open-source software that streamline image capture and image analysis with intuitive graphical user interfaces. RhizoVision Analyzer was physically validated using copper wire and features were extensively validated using 10,464 ground-truth simulated images of dicot and monocot root systems. This platform was then used to phenotype soybean and wheat root crowns. A total of 2,799 soybean (Glycine max) root crowns of 187 lines and 1,753 wheat (Triticum aestivum) root crowns of 186 lines were phenotyped. Principal component analysis indicated similar correlations among features in both species. The maximum heritability was 0.74 in soybean and 0.22 in wheat, indicating differences in species and populations need to be considered. The integrated RhizoVision Crown platform facilitates high-throughput phenotyping of crop root crowns, and sets a standard by which open plant phenotyping platforms can be benchmarked.

plant biology↗

bioRxiv2017

Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large scale genetic studies

BackgroundGenetic analyses of plant root system development require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming).\n\nFindingsWe trained a Random Forest algorithm to infer architectural traits from automatically-extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify Quantitative Trait Loci that had previously been discovered using a semi-automated method.\n\nConclusionsWe have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput in large scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other area of plant phenotyping.

plant biology↗