Biology subjects

Roselli, C.

Publications and source records attributed to Roselli, C..

12 recordsLinked to original sources

bioRxiv2026

The Wobble Uridine tRNA Writer MnmA Shapes Codon-Dependent Stress Response Systems

Escherichia coli uses wobble uridine (U34) modifications to tune codon decoding, but how individual tRNA writer enzymes shape gene expression remains unclear. Here, we identify MnmA, the U34 thiolation enzyme for tRNALys, tRNAGln, and tRNAGlu, as a central regulator linking codon-directed translation to regulon control and stress response. Loss of MnmA depleted s2U-dependent wobble modifications, preventing geranyl-(ges2U) and seleno-(se2U)-based modifications, causing growth defects, reduced catalase activity, and multi-level gene expression dysregulation. The {Delta}mnmA cells showed broad adaptive transcriptional reprogramming associated with RpoS- and OxyR-regulated pathways, which was accompanied by compromised protein output. Endogenous and tagged-protein analyses revealed specific impairment of transcriptional regulators, adaptive and detoxification proteins, including RpoS, OxyR, FliA, KatE, and KatG. Polysome profiling and polysome-associated RNA sequencing showed that MnmA deficiency globally reduces translational capacity and uncouples mRNA abundance from translational efficiency, which is exacerbated during oxidative stress. We developed genome-wide codon-usage mapping analytics to identify five codon-defined gene clusters, with specific clusters enriched for Lys, Gln, and Glu codons disproportionately affected by MnmA loss. Together, these findings support that wobble uridine thiolation and downstream modifications pair with corresponding codon architecture to coordinate the translation of regulon controllers and stress-response networks linked to bacterial fitness. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=159 HEIGHT=200 SRC="FIGDIR/small/745044v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@1a4058borg.highwire.dtl.DTLVardef@167f4cdorg.highwire.dtl.DTLVardef@1f8c7a8org.highwire.dtl.DTLVardef@1fc0767_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2026

Ataxin-2 regulates the synaptonemal complex to ensure chromosome pairing during female meiosis

Sexual reproduction relies on meiotic recombination and the accurate segregation of homologous chromosomes to generate viable, genetically diverse gametes. While the molecular mechanisms of recombination and chromosome segregation are well studied, the upstream regulatory cues that drive expression of key meiotic genes remain poorly understood, especially in metazoans. Emerging evidence suggests that post-transcriptional regulation plays a central role in initiating and coordinating the meiotic program in both fruit flies and mammals. Here, we identify the RNA-binding protein Ataxin-2 (Atx2) as a crucial regulator of meiosis in Drosophila melanogaster. We show that Atx2 positively regulates meiotic factors, especially components of the synaptonemal complex (SC), a structure essential for pairing, recombination and segregation of homologous chromosomes. In Atx2-depleted germ cells, SC component mRNA and protein levels are markedly reduced, leading to defective SC assembly and maintenance. Consequently, homologous chromosomes fail to pair properly, which is essential to ensure homolog segregation and prevent aneuploidy. These findings uncover Atx2 as a key regulator of the SC and highlight an underappreciated layer of gene regulation essential for accurate meiotic chromosome segregation and fertility.

genetics↗

bioRxiv2025

Intersecting experimental evolution and CRISPR screens to identify novel insecticide resistance loci

Understanding toxin resistance in insects is key to appreciate niche adaptations but remains challenging due to its often-polygenic basis. A well-known example is the specialized association of Drosophila sechellia with noni fruit (Morinda citrifolia), which is toxic to most other insects, including the closely-related Drosophila simulans and Drosophila melanogaster. Toxicity of noni is due to its high concentration of octanoic acid (OA), but the mechanisms that determine sensitivity or resistance to OA in different species remain poorly understood. Here, we experimentally-evolved D. simulans with increased OA resistance, identifying multiple loci under selection. Cross-referencing these with a genome-wide, OA-resistance CRISPR screen in a D. melanogaster cell line highlighted two proteins: Kraken, a putative detoxification enzyme expressed in digestive and renal tissues, and Alkbh7, a mitochondrial protein linked to fatty acid metabolism. Both genes show elevated expression in D. sechellia and OA-resistant D. simulans. In D. melanogaster, kraken mutants are more OA-sensitive, while Alkbh7 overexpression increased OA resistance. Importantly, mutation of these genes in D. sechellia reduced OA tolerance. Our identification of genes underlying OA resistance in laboratory and natural contexts demonstrates how complementary, cross-species selection approaches can provide insights into complex mechanisms of toxin susceptibility and adaptation; such methods could also have practical applications in the characterization of natural and artificial insecticides.

evolutionary biology↗

bioRxiv2025

The Ataxin-2 protein is required in Kenyon cells for RNP-granule assembly and appetitive long-term memory formation.

Ribonucleo-protein granules (mRNP granules) are thought to contribute to the control of neuronal mRNA translation required for consolidation of long-term memories. Consistent with this, the function of Ataxin-2 in mRNA granule assembly has been shown to be required for long-term olfactory habituation (LTH) in Drosophila, a form of non-associative memory. Knockdown of Ataxin-2 in either local interneurons (LNs) or projection neurons (PNs) of the insect antennal lobe disrupts LTH, leading to a model in which Ataxin-dependent translational control is required in both presynaptic and postsynaptic elements of the LN-PN synapse, whose potentiation has been causally linked to LTH. Here we use novel and established methods for cell-type specific perturbation to ask: (a) whether Ataxin-2 controls mRNA granule assembly in cell types beyond the few that have been examined; and (b) whether it functions not only in LTH, but also for long-term olfactory associative memory (LTM). We show that Ataxin-2 controls mRNP granule assembly in additional neuronal types, namely Kenyon Cells that encode associative memory, as well as more broadly in non-neuronal cells, e.g. in nurse cells in the egg chamber. Furthermore, selective knockdown of Atx2 in /{beta} and /{beta} KCs blocks appetitive long-term but not short-term associative memories. Taken together these observations support a hypothesis that Ataxin-2 dependent translational control is widely required across different mnemonic circuits for consolidation of respective forms of long-term memories.

neuroscience↗

bioRxiv2021

Genetic Analysis of Right Heart Structure and Function in 40,000 People

The heart evolved hundreds of millions of years ago. During mammalian evolution, the cardiovascular system developed with complete separation between pulmonary and systemic circulations incorporated into a single pump with chambers dedicated to each circulation. A lower pressure right heart chamber supplies deoxygenated blood to the lungs, while a high pressure left heart chamber supplies oxygenated blood to the rest of the body. Due to the complexity of morphogenic cardiac looping and septation required to form these two chambers, congenital heart diseases often involve maldevelopment of the evolutionarily recent right heart chamber. Additionally, some diseases predominantly affect structures of the right heart, including arrhythmogenic right ventricular cardiomyopathy (ARVC) and pulmonary hypertension. To gain insight into right heart structure and function, we fine-tuned deep learning models to recognize the right atrium, the right ventricle, and the pulmonary artery, and then used those models to measure right heart structures in over 40,000 individuals from the UK Biobank with magnetic resonance imaging. We found associations between these measurements and clinical disease including pulmonary hypertension and dilated cardiomyopathy. We then conducted genome-wide association studies, identifying 104 distinct loci associated with at least one right heart measurement. Several of these loci were found near genes previously linked with congenital heart disease, such as NKX2-5, TBX3, WNT9B, and GATA4. We also observed interesting commonalities and differences in association patterns at genetic loci linked with both right and left ventricular measurements. Finally, we found that a polygenic predictor of right ventricular end systolic volume was associated with incident dilated cardiomyopathy (HR 1.28 per standard deviation; P = 2.4E-10), and remained a significant predictor of disease even after accounting for a left ventricular polygenic score. Harnessing deep learning to perform large-scale cardiac phenotyping, our results yield insights into the genetic and clinical determinants of right heart structure and function.

genetics↗

bioRxiv2020

Rare Genetic Variation Underlying Human Diseases and Traits: Results from 200,000 Individuals in the UK Biobank

BackgroundMany human diseases are known to have a genetic contribution. While genome-wide studies have identified many disease-associated loci, it remains challenging to elucidate causal genes. In contrast, exome sequencing provides an opportunity to identify new disease genes and large-effect variants of clinical relevance. We therefore sought to determine the contribution of rare genetic variation in a curated set of human diseases and traits using a unique resource of 200,000 individuals with exome sequencing data from the UK Biobank. Methods and ResultsWe included 199,832 participants with a mean age of 68 at follow-up. Exome-wide gene-based tests were performed for 64 diseases and 23 quantitative traits using a mixed-effects model, testing rare loss-of-function and damaging missense variants. We identified 51 known and 23 novel associations with 26 diseases and traits at a false-discovery-rate of 1%. There was a striking risk associated with many Mendelian disease genes including: MYPBC3 with over a 100-fold increased odds of hypertrophic cardiomyopathy, PKD1 with a greater than 25-fold increased odds of chronic kidney disease, and BRCA2, BRCA1, ATM and PALB2 with 3 to 10-fold increased odds of breast cancer. Notable novel findings included an association between GIGYF1 and type 2 diabetes (OR 5.6, P=5.35x10-8), elevated blood glucose, and lower insulin-like-growth-factor-1 levels. Rare variants in CCAR2 were also associated with diabetes risk (OR 13, P=8.5x10-8), while COL9A3 was associated with cataract (OR 3.4, P=6.7x10-8). Notable associations for blood lipids and hypercholesterolemia included NR1H3, RRBP1, GIGYF1, SCGN, APH1A, PDE3B and ANGPTL8. A number of novel genes were associated with height, including DTL, PIEZO1, SCUBE3, PAPPA and ADAMTS6, while BSN was associated with body-mass-index. We further assessed putatively pathogenic variants in known Mendelian cardiovascular disease genes and found that between 1.3 and 2.3% of the population carried likely pathogenic variants in known cardiomyopathy, arrhythmia or hypercholesterolemia genes. ConclusionsLarge-scale population sequencing identifies known and novel genes harboring high-impact variation for human traits and diseases. A number of novel findings, including GIGYF1,represent interesting potential therapeutic targets. Exome sequencing at scale can identify a meaningful proportion of the population that carries a pathogenic variant underlying cardiovascular disease.

genetics↗

bioRxiv2020

Deep learning enables genetic analysis of the human thoracic aorta

The aorta is the largest blood vessel in the body, and enlargement or aneurysm of the aorta can predispose to dissection, an important cause of sudden death. While rare syndromes have been identified that predispose to aortic aneurysm, the common genetic basis for the size of the aorta remains largely unknown. By leveraging a deep learning architecture that was originally developed to recognize natural images, we trained a model to evaluate the dimensions of the ascending and descending thoracic aorta in cardiac magnetic resonance imaging. After manual annotation of just 116 samples, we applied this model to 3,840,140 images from the UK Biobank. We then conducted a genome-wide association study in 33,420 individuals, revealing 68 loci associated with ascending and 35 with descending thoracic aortic diameter, of which 10 loci overlapped. Integration of common variation with transcriptome-wide analyses, rare-variant burden tests, and single nucleus RNA sequencing prioritized SVIL, a gene highly expressed in vascular smooth muscle, that was significantly associated with the diameter of the ascending and descending aorta. A polygenic score for ascending aortic diameter was associated with a diagnosis of thoracic aortic aneurysm in the remaining 391,251 UK Biobank participants who did not undergo imaging (HR = 1.44 per standard deviation; P = 3.7{middle dot}10-12). Defining the genetic basis of the diameter of the aorta may enable the identification of asymptomatic individuals at risk for aneurysm or dissection and facilitate the prioritization of potential therapeutic targets for the prevention or treatment of aortic aneurysm. Finally, our results illustrate the potential for rapidly defining novel quantitative traits derived from a deep learning model, an approach that can be more broadly applied to biomedical imaging data.

genomics↗

bioRxiv2020

Transcriptional and Cellular Diversity of the Human Heart

IntroductionThe human heart requires a complex ensemble of specialized cell types to perform its essential function. A greater knowledge of the intricate cellular milieu of the heart is critical to increase our understanding of cardiac homeostasis and pathology. As recent advances in low input RNA-sequencing have allowed definitions of cellular transcriptomes at single cell resolution at scale, here we have applied these approaches to assess the cellular and transcriptional diversity of the non-failing human heart. MethodsMicrofluidic encapsulation and barcoding was used to perform single nuclear RNA sequencing with samples from seven human donors, selected for their absence of overt cardiac disease. Individual nuclear transcriptomes were then clustered based upon transcriptional profiles of highly variable genes. These clusters were used as the basis for between-chamber and between-sex differential gene expression analyses and intersection with genetic and pharmacologic data ResultsWe sequenced the transcriptomes of 287,269 single cardiac nuclei, revealing a total of 9 major cell types and 20 subclusters of cell types within the human heart. Cellular subclasses include two distinct groups of resident macrophages, four endothelial subtypes, and two fibroblasts subsets. Comparisons of cellular transcriptomes by cardiac chamber or sex reveal diversity not only in cardiomyocyte transcriptional programs, but also in subtypes involved in extracellular matrix remodeling and vascularization. Using genetic association data, we identified strong enrichment for the role of cell subtypes in cardiac traits and diseases. Finally, intersection of our dataset with genes on cardiac clinical testing panels and the druggable genome reveals striking patterns of cellular specificity. ConclusionsUsing large-scale single nuclei RNA sequencing, we have defined the transcriptional and cellular diversity in the normal human heart. Our identification of discrete cell subtypes and differentially expressed genes within the heart will ultimately facilitate the development of new therapeutics for cardiovascular diseases.

genomics↗

bioRxiv2019

Multi-ancestry GWAS of the electrocardiographic PR interval identifies 210 loci underlying cardiac conduction

The electrocardiographic PR interval reflects atrioventricular conduction, and is associated with conduction abnormalities, pacemaker implantation, atrial fibrillation (AF), and cardiovascular mortality1,2. We performed multi-ancestry (N=293,051) and European only (N=271,570) genome-wide association (GWAS) meta-analyses for the PR interval, discovering 210 loci of which 149 are novel. Variants at all loci nearly doubled the percentage of heritability explained, from 33.5% to 62.6%. We observed enrichment for genes involved in cardiac muscle development/contraction and the cytoskeleton highlighting key regulation processes for atrioventricular conduction. Additionally, 19 novel loci harbour genes underlying inherited monogenic heart diseases suggesting the role of these genes in cardiovascular pathology in the general population. We showed that polygenic predisposition to PR interval duration is an endophenotype for cardiovascular disease risk, including distal conduction disease, AF, atrioventricular pre-excitation, non-ischemic cardiomyopathy, and coronary heart disease. These findings advance our understanding of the polygenic basis of cardiac conduction, and the genetic relationship between PR interval duration and cardiovascular disease.

genetics↗

bioRxiv2019

Detailed Regulatory Interaction Map of the Human Heart Facilitates Gene Discovery for Cardiovascular Disease

Most disease-associated variants identified by population based genetic studies are non-coding, which compromises finding causative genes and mechanisms. Presumably they interact through looping with nearby genes to modulate transcription. Hi-C provides the most complete and unbiased method for genome-wide identification of potential regulatory interactions, but finding chromatin loops in Hi-C data remains difficult and tissue specific data are limited. We have generated Hi-C data from primary cardiac tissue and developed a method, peakHiC, for sensitive and quantitative loop calling to uncover the human heart regulatory interactome. We identify complex CTCF-dependent and -independent contact networks, with loops between coding and non-coding gene promoters, shared enhancers and repressive sites. Across the genome, enhancer interaction strength correlates with gene transcriptional output and loop dynamics follows CTCF, cohesin and H3K27Ac occupancy levels. Finally, we demonstrate that intersection of the human heart regulatory interactome with cardiovascular disease variants facilitates prioritizing disease-causative genes.

genomics↗

bioRxiv2019

Genome-wide association study provides new insights into the genetic architecture and pathogenesis of heart failure

Heart failure (HF) is a leading cause of morbidity and mortality worldwide1. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained2-4. We report the largest GWAS meta-analysis of HF to-date, comprising 47,309 cases and 930,014 controls. We identify 12 independent variant associations with HF at 11 genomic loci, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function suggesting shared genetic aetiology. Expression quantitative trait analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homeostasis (BAG3), and cellular senescence (CDKN1A). Using Mendelian randomisation analysis we provide new evidence supporting previously equivocal causal roles for several HF risk factors identified in observational studies, and demonstrate CAD-independent effects for atrial fibrillation, body mass index, hypertension and triglycerides. These findings extend our knowledge of the genes and pathways underlying HF and may inform the development of new therapeutic approaches.

genetics↗

bioRxiv2017

Genome-wide polygenic score to identify a monogenic risk-equivalent for coronary disease

Identification of individuals at increased genetic risk for a complex disorder such as coronary disease can facilitate treatments or enhanced screening strategies. A rare monogenic mutation associated with increased cholesterol is present in ~1:250 carriers and confers an up to 4-fold increase in coronary risk when compared with non-carriers. Although individual common polymorphisms have modest predictive capacity, their cumulative impact can be aggregated into a polygenic score. Here, we develop a new, genome-wide polygenic score that aggregates information from 6.6 million common polymorphisms and show that this score can similarly identify individuals with a 4-fold increased risk for coronary disease. In >400,000 participants from UK Biobank, the score conforms to a normal distribution and those in the top 2.5% of the distribution are at 4-fold increased risk compared to the remaining 97.5%. Similar patterns are observed with genome-wide polygenic scores for two additional diseases - breast cancer and severe obesity.\n\nOne Sentence SummaryA genome-wide polygenic score identifies 2.5% of the population born with a 4-fold increased risk for coronary artery disease.

genomics↗