Biology subjects

Wood, A. M.

Publications and source records attributed to Wood, A. M..

6 recordsLinked to original sources

bioRxiv2026

Comparative analysis of 15 chromosome-scale near T2T assemblies of Brazilian Fusarium graminearum isolates

Fusarium graminearum is the causative agent of Fusarium head blight of wheat, yet genomic resources for Brazilian populations, where it is the dominant species, remain limited to a single reference genome. We report fifteen near telomere-to-telomere (T2T) assemblies of Brazilian F. graminearum isolates generated with Oxford Nanopore sequencing. Each genome was assembled into four nuclear chromosomes, with 118 of 120 telomeres resolved, BUSCO completeness of 99.199.3%, and 14,80214,894 predicted genes per isolate. The genomes are structurally conserved, showing no chromosome-scale rearrangements and uniform transposable element content. Orthology assignment placed 90.4% of genes in the core genome. This suggests that the large phenotypic variance in aggressiveness observed between isolates likely resides in regulatory differences and/or within the small and largely uncharacterised accessory fraction. Ultimately, these assemblies provide a high-resolution resource for the global FHB community and a foundation for comparative analysis of a previously underrepresented population.

bioinformatics↗

bioRxiv2026

Cytoskeletal engineering through Formin-like 1 overexpression enhances T cell infiltration and antitumor potency in solid tumors

Solid tumors are often surrounded by abnormal vasculature and a dense collagen-rich extracellular matrix that severely restrict the infiltration of T cells, including tumor-infiltrating lymphocytes (TILs) and chimeric antigen receptor (CAR)-T cells. These physical barriers represent a major obstacle to the efficacy of adoptive T cell therapies in solid tumors. We previously identified Formin-like 1 (FMNL1) as a cytoskeletal regulator critical for T cell extravasation and migration through restrictive environments, making it a promising target to improve T cell infiltration into tumors. Here, we developed a bioengineering platform to enhance T cell cytoskeletal dynamics by overexpressing FMNL1 in TILs and CAR-T cells. FMNL1 overexpression significantly increased T cell migration through restrictive pores in transwell assays, supporting enhanced migratory capacity of T cells under mechanically constraining conditions. Importantly, FMNL1 overexpression did not impair T cell reactivation or cytotoxic function in vitro. In murine models of melanoma and lung carcinoma characterized by limited effector T cell infiltration, FMNL1-overexpressing TILs and CAR-T cells had significantly increased accumulation at tumor sites compared to controls. Importantly, enhanced tumor accumulation resulted in improved therapeutic activity, as adoptive transfer of FMNL1-overexpressing CAR-T cells limited tumor growth and prolonged the survival of tumor-bearing mice in multiple melanoma models. Together, our findings identify FMNL1 as a broadly applicable cytoskeletal engineering target to enhance T cell accumulation and persistence in restrictive tumor microenvironments, thereby overcoming a fundamental limitation of adoptive cellular immunotherapy in solid tumors.

immunology↗

bioRxiv2025

Chemoenzymatic synthesis of sialylated and fucosylated mucin analogs reveals glycan-dependent effects on protein conformation and degradation.

Mucin proteins are essential for life but are challenging to study due to their complex glycosylation patterns. Synthetic mimics have become vital tools for understanding and modulating the roles of mucins in human health and disease. We developed a chemoenzymatic approach to prepare polypeptide-based synthetic mucins displaying a variety of glycans with native linkages and orientations. By combining the polymerization of glycosylated amino acid N-carboxyanhydrides with enzymatic sialylation and fucosylation, we produced a tunable panel of synthetic mucins. These polymers were recognized by natural glycan-binding and glycan-degrading enzymes, providing insights into the structural preferences of these proteins. Glycan- and linkage-dependent effects on proteolysis were observed. Further, investigation of the influence of glycans on peptide backbone secondary structure revealed that both sialylation and linkage at Ser vs. Thr have profound effects on hierarchical conformation. Overall, our methodology offers versatile tools for exploring the diverse glycobiology of mucins.

bioengineering↗

bioRxiv2020

Cross-platform genetic discovery of small molecule products of metabolism and application to clinical outcomes

Circulating levels of small molecules or metabolites are highly heritable, but the impact of genetic differences in metabolism on human health is not well understood. In this cross-platform, genome-wide meta-analysis of 174 metabolite levels across six cohorts including up to 86,507 participants (70% unpublished data), we identify 499 (362 novel) genome-wide significant associations (p<4.9×10-10) at 144 (94 novel) genomic regions. We show that inheritance of blood metabolite levels in the general population is characterized by pleiotropy, allelic heterogeneity, rare and common variants with large effects, non-linear associations, and enrichment for nonsynonymous variation in transporter and enzyme encoding genes. The majority of identified genes are known to be involved in biochemical processes regulating metabolite levels and to cause monogenic inborn errors of metabolism linked to specific metabolites, such as ASNS (rs17345286, MAF=0.27) and asparagine levels. We illustrate the influence of metabolite-associated variants on human health including a shared signal at GLP2R (p.Asp470Asn) associated with higher citrulline levels, body mass index, fasting glucose-dependent insulinotropic peptide and type 2 diabetes risk, and demonstrate beta-arrestin signalling as the underlying mechanism in cellular models. We link genetically-higher serine levels to a 95% reduction in the likelihood of developing macular telangiectasia type 2 [odds ratio (95% confidence interval) per standard deviation higher levels 0.05 (0.03-0.08; p=9.5×10-30)]. We further demonstrate the predictive value of genetic variants identified for serine or glycine levels for this rare and difficult to diagnose degenerative retinal disease [area under the receiver operating characteristic curve: 0.73 (95% confidence interval: 0.70-0.75)], for which low serine availability, through generation of deoxysphingolipids, has recently been shown to be causally relevant. These results show that integration of human genomic variation with circulating small molecule data obtained across different measurement platforms enables efficient discovery of genetic regulators of human metabolism and translation into clinical insights.Competing Interest StatementA.S.B. has received grants from AstraZeneca, Biogen, Bioverativ, Merck, Novartis, and Sanofi. J. D. sits on the International Cardiovascular and Metabolic Advisory Board for Novartis (since 2010), the Steering Committee of UK Biobank (since 2011), the MRC International Advisory Group (ING) member, London (since 2013), the MRC High Throughput Science Omics Panel Member, London (since 2013), the Scientific Advisory Committee for Sanofi (since 2013), the International Cardiovascular and Metabolism Research and Development Portfolio Committee for Novartis and the Astra Zeneca Genomics Advisory Board (2018). E.B.F. is an employee and stock holder of Pfizer.View Full Text

genetics↗

bioRxiv2018

Genomic risk prediction of coronary artery disease in nearly 500,000 adults: implications for early screening and primary prevention

BackgroundCoronary artery disease (CAD) has substantial heritability and a polygenic architecture; however, genomic risk scores have not yet leveraged the totality of genetic information available nor been externally tested at population-scale to show potential utility in primary prevention.\n\nMethodsUsing a meta-analytic approach to combine large-scale genome-wide and targeted genetic association data, we developed a new genomic risk score for CAD (metaGRS), consisting of 1.7 million genetic variants. We externally tested metaGRS, individually and in combination with available conventional risk factors, in 22,242 CAD cases and 460,387 non-cases from UK Biobank.\n\nFindingsIn UK Biobank, a standard deviation increase in metaGRS had a hazard ratio (HR) of 1.71 (95% CI 1.68-1.73) for CAD, greater than any other externally tested genetic risk score. Individuals in the top 20% of the metaGRS distribution had a HR of 4.17 (95% CI 3.97-4.38) compared with those in the bottom 20%. The metaGRS had higher C-index (C=0.623, 95% CI 0.615-0.631) for incident CAD than any of four conventional factors (smoking, diabetes, hypertension, and body mass index), and addition of the metaGRS to a model of conventional risk factors increased C-index by 3.7%. In individuals on lipid-lowering or anti-hypertensive medications at recruitment, metaGRS hazard for incident CAD was significantly but only partially attenuated with HR of 2.83 (95% CI 2.61- 3.07) between the top and bottom 20% of the metaGRS distribution.\n\nInterpretationRecent genetic association studies have yielded enough information to meaningfully stratify individuals using the metaGRS for CAD risk in both early and later life, thus enabling targeted primary intervention in combination with conventional risk factors. The metaGRS effect was partially attenuated by lipid and blood pressure-lowering medication, however other prevention strategies will be required to fully benefit from earlier genomic risk stratification.\n\nFundingNational Health and Medical Research Council of Australia, British Heart Foundation, Australian Heart Foundation.

genetics↗

bioRxiv2017

Consequences Of Natural Perturbations In The Human Plasma Proteome

Proteins are the primary functional units of biology and the direct targets of most drugs, yet there is limited knowledge of the genetic factors determining inter-individual variation in protein levels. Here we reveal the genetic architecture of the human plasma proteome, testing 10.6 million DNA variants against levels of 2,994 proteins in 3,301 individuals. We identify 1,927 genetic associations with 1,478 proteins, a 4-fold increase on existing knowledge, including trans associations for 1,104 proteins. To understand consequences of perturbations in plasma protein levels, we introduce an approach that links naturally occurring genetic variation with biological, disease, and drug databases. We provide insights into pathogenesis by uncovering the molecular effects of disease-associated variants. We identify causal roles for protein biomarkers in disease through Mendelian randomization analysis. Our results reveal new drug targets, opportunities for matching existing drugs with new disease indications, and potential safety concerns for drugs under development.

genomics↗