Biology subjects

Moran, K.

Publications and source records attributed to Moran, K..

5 recordsLinked to original sources

bioRxiv2024

The evolution of wasp mimicry and biogeography in the genus Temnostoma (Diptera: Syrphidae)

One of the most remarkable examples of Batesian mimicry occurs in the genus Temnostoma Le Peletier & Audinet-Serville, 1828 (Diptera: Syrphidae). Adults of this genus have an overall resemblance to hymenopterans combined with behavioural mimicry (they move the fore legs in front of the head mimicking hymenopteran antennae). While some species of Temnostoma are considered perfect mimics of social wasps, other species have a darker colour pattern and are rather imperfect mimics. Both colour morphs are widespread through the Holarctic. Here, we infer for the first time the evolutionary history of the genus with special focus on the evolution of mimicry and biogeography. With material covering 75 % of known species of Temnostoma and both colour morphs from each biogeographical region, we inferred a molecular phylogeny based on six molecular markers (mitochondrial protein-coding COI gene, nuclear 28s rRNA gene, and four nuclear protein-coding genes: AATS, CK1, TULP, and RBP-15). Using Bayesian inference, we obtained a highly resolved phylogenetic tree supporting the monophyly of the genus Temnostoma as a sister group of genus Takaomyia Herve-Bazin, 1914. Within Temnostoma, Te. daochus and Te. barberi (two Nearctic species with strikingly different mimicry patterns) were found to be closely related to each other and together form a lineage sister to the rest of the genus. Our results suggest that the behavioural mimicry of wasp antennae is a plesiomorphic state inherited from a common ancestor that includes the genera Temnostoma and Takaomyia. Within Temnostoma, the dark colour pattern (imperfect mimicry) appeared to be an ancestral state and perfect wasp mimicry evolved two times independently within the genus. In some species inhabiting northern parts of the Holarctic, secondary darkening and consequent degradation of the wasp mimicry appeared. This indicates high evolutionary plasticity and ongoing selection pressure on morphological characters related to mimicry in hover flies.

zoology↗

bioRxiv2023

In vitro validated antibody design against multiple therapeutic antigens using generative inverse folding

Deep learning approaches have demonstrated the ability to design protein sequences given backbone structures [1, 2, 3, 4, 5]. While these approaches have been applied in silico to designing antibody complementarity-determining regions (CDRs), they have yet to be validated in vitro for designing antibody binders, which is the true measure of success for antibody design. Here we describe IgDesign, a deep learning method for antibody CDR design, and demonstrate its robustness with successful binder design for 8 therapeutic antigens. The model is tasked with designing heavy chain CDR3 (HCDR3) or all three heavy chain CDRs (HCDR123) using native backbone structures of antibody-antigen complexes, along with the antigen and antibody framework (FWR) sequences as context. For each of the 8 antigens, we design 100 HCDR3s and 100 HCDR123s, scaffold them into the native antibodys variable region, and screen them for binding against the antigen using surface plasmon resonance (SPR). As a baseline, we screen 100 HCDR3s taken from the models training set and paired with the native HCDR1 and HCDR2. We observe that both HCDR3 design and HCDR123 design outperform this HCDR3-only baseline. IgDesign is the first experimentally validated antibody inverse folding model. It can design antibody binders to multiple therapeutic antigens with high success rates and, in some cases, improved affinities over clinically validated reference antibodies. Antibody inverse folding has applications to both de novo antibody design and lead optimization, making IgDesign a valuable tool for accelerating drug development and enabling therapeutic design. The data generated in this study serve as a useful benchmark of diverse antibody-antigen interactions. We use this data to benchmark self-consistency RMSD (scRMSD), using ABodyBuilder2 [6], ABodyBuilder3 [7], and ESMFold [8], as a metric for assessing binding. We open source the code for IgDesign and the SPR datasets. Code and datasets can be found at https://github.com/AbSciBio/igdesign.

synthetic biology↗

bioRxiv2023

Unlocking de novo antibody design with generative artificial intelligence

Generative AI has the potential to redefine the process of therapeutic antibody discovery. In this report, we describe and validate deep generative models for the de novo design of antibodies against human epidermal growth factor receptor (HER2) without additional optimization. The models enabled an efficient workflow that combined in silico design methods with high-throughput experimental techniques to rapidly identify binders from a library of [~]106 heavy chain complementarity-determining region (HCDR) variants. We demonstrated that the workflow achieves binding rates of 10.6% for HCDR3 and 1.8% for HCDR123 designs and is statistically superior to baselines. We further characterized 421 diverse binders using surface plasmon resonance (SPR), finding 71 with low nanomolar affinity similar to the therapeutic anti-HER2 antibody trastuzumab. A selected subset of 11 diverse high-affinity binders were functionally equivalent or superior to trastuzumab, with most demonstrating suitable developability features. We designed one binder with [~]3x higher cell-based potency compared to trastuzumab and another with improved cross-species reactivity1. Our generative AI approach unlocks an accelerated path to designing therapeutic antibodies against diverse targets.

synthetic biology↗

bioRxiv2020

Vaginal Microbiome Topic Modelling of Laboring Ugandan Women With and Without Fever

The composition of the maternal vaginal microbiome may influence the duration of pregnancy, onset of labor and even neonatal outcomes. Maternal microbiome research in sub Saharan-Africa has focused on non-pregnant and postpartum composition of the vaginal microbiome. We examined the vaginal microbiome composition of 99 laboring Ugandan women using routine microbiology and 16S ribosomal DNA sequencing from two hypervariable regions (V1-V2 and V3-V4), using standard hierarchical methods. We then introduce Grades of Membership (GoM) modeling for the vaginal microbiome, a method often used in the text mining machine learning literature. Leveraging GoM models, we create a basis composed of a small number of microbial topics whose linear combination optimally represents each patient yielding more accurate associations. We identified relationships between defined communities and the presentation or absence of intrapartum fever. Using a random forest model we showed that by including novel microbial topic models we improved upon clinical variables to predict maternal fever. We also show by integrating clinical variables with a microbial topic model into this model found young maternal age, fever report earlier in the current pregnancy, and longer labors, as well as a more diverse, less Lactobacillus dominated microbiome were features of labor associated with intrapartum fever. These results better define relationships between presentation or absence of intrapartum fever, demographics, peripartum course, and vaginal microbial communities, and improve our understanding of the impact of the microbiome on maternal and neonatal infection risk.

genomics↗

bioRxiv2019

Improving Probabilistic Infectious Disease Forecasting Through Coherence

With an estimated $10.4 billion in medical costs and 31.4 million outpatient visits each year, influenza poses a serious burden of disease in the United States. To provide insights and advance warning into the spread of influenza, the U.S. Centers for Disease Control and Prevention (CDC) runs a challenge for forecasting weighted influenza-like illness (wILI) at the national and regional level. Many models produce independent forecasts for each geographical unit, ignoring the constraint that the national wILI is a weighted sum of regional wILI, where the weights correspond to the population size of the region. We propose a novel algorithm that transforms a set of independent forecast distributions to obey this constraint, which we refer to as probabilistically coherent. Enforcing probabilistic coherence led to an increase in forecast skill for 90% of the models we tested over multiple flu seasons, highlighting the importance of respecting the forecasting systems geographical hierarchy. Author SummarySeasonal influenza causes a significant public health burden nationwide. Accurate influenza forecasting may help public health officials allocate resources and plan responses to emerging outbreaks. The U.S. Centers for Disease Control and Prevention (CDC) reports influenza data at multiple geographical units, including regionally and nationally, where the national data are by construction a weighted sum of the regional data. In an effort to improve influenza forecast accuracy across all models submitted to the CDCs annual flu forecasting challenge, we examined the effect of imposing this geographical constraint on the set of independent forecasts, made publicly available by the CDC. We developed a novel method to transform forecast densities to obey the geographical constraint that respects the correlation structure between geographical units. This method showed consistent improvement across 90% of models and that held when stratified by targets and test seasons. Our method can be applied to other forecasting systems both within and outside an infectious disease context that have a geographical hierarchy.

bioinformatics↗