Michele M Mulholland
email: mmmulholland@mdanderson.org

A Meguerditchian

& Hopkins

Michale E Keeling

Age-and sex-related differences in baboon (Papio anubis) gray matter covariation

Keywords: aging, nonhuman primate, baboon, source-based morphometry, gray matter covariation

Age-related changes in cognition, brain morphology, and behavior are exhibited in several primate species. Baboons, like humans, naturally develop Alzheimer's disease-like pathology and cognitive declines with age and are an underutilized model for studies of aging. To determine age-related differences in gray matter covariation of 89 olive baboons (Papio anubis), we used source-based morphometry (SBM) to analyze data from magnetic resonance images. We hypothesized that we would find significant age effects in one or more SBM components, particularly those which include regions influenced by age in humans and other nonhuman primates (NHPs). A multivariate analysis of variance revealed that individual weighted gray matter covariation scores differed across the age classes. Elderly baboons contributed significantly less to gray matter covariation components including the brainstem, superior parietal cortex, thalamus, and pallidum compared to juveniles, and middle and superior frontal cortex compared to juveniles and young adults (p<0.05). Future studies should examine the relationship between the changes in gray matter covariation reported here and age-related cognitive decline.

Introduction

Both humans and nonhuman primates show changes in cognition, brain morphology, and behavior with age, both as a part of normal aging and as a result of age-related disease. Normal human aging is characterized by declines in cognition, global brain volume, gray matter volume, and functional activation associated with increasing age [START_REF] Abe | Aging in the CNS: Comparison of gray/white matter volume and diffusion tensor data[END_REF][START_REF] Bigler | Temporal lobe morphology in normal aging and traumatic brain injury[END_REF][START_REF] Bigler | Hippocampal volume in normal aging and traumatic brain injury[END_REF][START_REF] Bishop | Neural mechanisms of ageing and cognitive decline[END_REF][START_REF] Brickman | Structural MRI covariance patterns associated with normal aging and neuropsychological functioning[END_REF][START_REF] Chen | Brain aging in humans, chimpanzees (Pan troglodytes), and rhesus macaques (Macaca mulatta): magnetic resonance imaging studies of macro-and microstructural changes[END_REF][START_REF] Ge | Age-related total gray matter and white matter changes in normal adult brain. Part I: Volumetric MR imaging analysis[END_REF][START_REF] Good | A voxelbased morphometric study of aging in 465 normal adult brains[END_REF][START_REF] Hawkins | Diffusion Tensor Imaging Correlates of Cognitive-Motor Decline in Normal Aging and Increased Alzheimer's Disease Risk[END_REF]. Most studies on nonhuman aging have utilized non-primate model species such as yeast, nematodes, fruit flies, and mice due mostly to availability, cost, short lifespan and well-documented genetic and/or physiological characteristics. While these models have provided important data and have advanced our understanding of aging, they do not closely model the human aging process. Utilizing nonhuman primate models (particularly those more closely related phylogenetically) is critically important given their genetic similarities to humans, longer lifespans, larger and more complex brains and more sophisticated cognitive abilities compared to other model species. Among nonhuman primates, baboons are particularly well-suited as a model for human aging. First, unlike other monkey species, there is evidence that baboons undergo well-coordinated growth, growth spurts, and an order of growth cessation like humans [START_REF] Leigh | Growth and development of baboons, The baboon in biomedical research[END_REF]. Second, the baboon genome is very similar to humans with over 90% homology of coding segments and 85% homology of non-coding regions. For this reason, the baboon has served as a model to study the genetics and epigenetics of several human diseases [START_REF] Cox | Baboons as a Model to Study Genetics and Epigenetics of Human Disease[END_REF][START_REF] Cox | A second-generation genetic linkage map of the baboon (Papio hamadryas) genome[END_REF][START_REF] Robinson | Analysis of 100 high-coverage genomes from a pedigreed captive baboon colony[END_REF][START_REF] Rogers | The Development and Status of the Baboon Genetic Linkage Map[END_REF][START_REF] Rogers | A Genetic Linkage Map of the Baboon (Papio hamadryas) Genome Based on Human Microsatellite Polymorphisms[END_REF]. In addition, both human and baboon lifespan is moderately heritable (0.23-0.26 and 0.23, respectively) and differences in frailty likely causes variation in individual mortality risk within both species [START_REF] Bronikowski | The aging baboon: Comparative demography in a non-human primate[END_REF][START_REF] Martin | Lifespan in captive baboons is heritable[END_REF]. Third, baboons have been shown to develop similar age-related diseases and conditions as humans, including osteoporosis and arthritis, menopause, endometriosis, obesity, diabetes, cardiovascular disease, and Alzheimer's-like pathology [START_REF] Martin | Lifespan in captive baboons is heritable[END_REF]Schultz et al., 2000a;Schultz et al., 2000b;[START_REF] Schultz | Tau pathology in neurons and glial cells of aged baboons[END_REF][START_REF] Yeung | A Cross-Sectional Study of Ageing and Cardiovascular Function over the Baboon Lifespan[END_REF]. For example, postmortem studies have shown that baboons develop tau pathology in both neurons and glia and Aβ plaques as they age (Schultz et al., 2000a;[START_REF] Schultz | The brain of the aging baboon: A nonhuman primate model for neuronal and glial tau pathology, Aging in nonhuman primates[END_REF]Schultz et al., 2000b;[START_REF] Schultz | Tau pathology in neurons and glial cells of aged baboons[END_REF].

Furthermore, compared to rhesus macaques (a common nonhuman primate model species), baboons have larger brains making them ideal for imaging studies [START_REF] Black | Neuroimaging in baboons, The baboon in biomedical research[END_REF], and the baboon prefrontal cortex is more developed and more similar to the human prefrontal cortex than the rhesus macaque [START_REF] Brent | The study of captive baboon behavior[END_REF][START_REF] Fridman | Species of the genus Papio (Cercopithecidae, primates) as subjects of biomedical research: II. Quantitative characteristics of contemporary use of baboon species in medical and biological investigations[END_REF]. Due to these characteristics, studies of baboon aging can help bridge the translational gap between other model species and human aging studies.

Like humans, there is evidence of age-related cognitive decline in baboons. In a cross-sectional study of 19 baboons (aged 1-14), [START_REF] Bonté | Age effects on transfer index performance and executive control in baboons (Papio papio)[END_REF] found that executive control (as measured by a transfer index task) declined with age. In another cross-sectional study of 6 baboons, older adults (20-23 years old) performed significantly worse on cognitive tasks (specifically, learning a novel task, precision, and simple discrimination) compared to younger adults (13-16 years old) [START_REF] Lizarraga | Age-related cognitive decline in baboons: modeling the prodromal phase of Alzheimer's disease and related dementias[END_REF]. These findings suggest that there are likely neurobiological changes occurring as baboons age. Although baboon neuroanatomy and function is well-studied (e.g., [START_REF] Amiez | Sulcal organization in the medial frontal cortex provides insights into primate brain evolution[END_REF][START_REF] Atkinson | Cortical folding of the primate brain: An interdisciplinary examination of the genetic architecture, modularity, and evolvability of a significant neurological trait in pedigreed baboons (Genus Papio)[END_REF][START_REF] Black | Neuroimaging in baboons, The baboon in biomedical research[END_REF][START_REF] Blaizot | The Parahippocampal Gyrus in the Baboon: Anatomical, Cytoarchitectonic and Magnetic Resonance Imaging (MRI) Studies[END_REF][START_REF] Cain | An anatomical asymmetry in the baboon brain[END_REF][START_REF] Greer | MR atlas of the baboon brain for functional neuroimaging[END_REF][START_REF] Love | The average baboon brain: MRI templates and tissue probability maps from 89 individuals[END_REF][START_REF] Marie | Left Brain Asymmetry of the Planum Temporale in a Nonhominid Primate: Redefining the Origin of Brain Specialization for Language[END_REF][START_REF] Mcbride | A comparative volumetric analysis of the prefrontal cortex in human and baboon MRI[END_REF][START_REF] Meguerditchian | Baboons (Papio anubis) living in larger social groups have bigger brains[END_REF][START_REF] Rogers | Heritability of brain volume, surface area and shape: An MRI study in an extended pedigree of baboons[END_REF][START_REF] Westerhausen | Corpus callosum morphology across the lifespan in baboons (Papio anubis): A cross-sectional study of relative mid-sagittal surface area and thickness[END_REF], and postmortem studies reveal the development of tau pathology as they age (Schultz et al., 2000a;[START_REF] Schultz | The brain of the aging baboon: A nonhuman primate model for neuronal and glial tau pathology, Aging in nonhuman primates[END_REF]Schultz et al., 2000b;[START_REF] Schultz | Tau pathology in neurons and glial cells of aged baboons[END_REF], to date there have been few in vivo neuroimaging studies focused specifically on the aging baboon brain. Franke et al. [START_REF] Franke | Premature Brain Aging in Baboons Resulting from Moderate Fetal Undernutrition[END_REF] found that prenatal undernutrition (70% of normal daily food intake) had long-term effects on the baboon brain, prematurely aging the young adult female baboon brain by 2.7 years on average. In addition, the authors examined typical brain aging across the baboon lifespan and reported a significant decrease in overall gray matter volume and increase in overall white matter volume with age.

More recently, [START_REF] Westerhausen | Corpus callosum morphology across the lifespan in baboons (Papio anubis): A cross-sectional study of relative mid-sagittal surface area and thickness[END_REF] examined corpus callosum morphology across the lifespan and found that the area of the corpus collosum increases slowly with age, particularly within the anterior section.

Here we examine the relationship between age and whole-brain gray matter covariation using source-based morphometry (SBM). SBM is a multivariate approach which incorporates an independent components analysis to create networks of brain regions that covary among gray or white matter voxels across the whole brain [START_REF] Gupta | Source-based morphometry: a decade of covarying structural brain patterns[END_REF]. Here we examine the contribution of both age and sex to gray matter covariation within the baboon brain. A number of studies in human subjects have reported age-related changes in gray matter covariation networks [START_REF] Dupre | Structural covariance networks across the life span, from 6 to 94 years of age[END_REF][START_REF] Evans | Networks of anatomical covariance[END_REF][START_REF] Hafkemeijer | Associations between age and gray matter volume in anatomical brain networks in middle-aged to older adults[END_REF][START_REF] Koini | Grey-matter network disintegration as predictor of cognitive and motor function with aging[END_REF][START_REF] Liu | Structural brain network changes across the adult lifespan[END_REF][START_REF] Montembeault | The impact of aging on gray matter structural covariance networks[END_REF]Xu et al., 2009b), but there are only a few studies of this type in nonhuman primates. Source-based morphometry-like methods were used to show that older rhesus macaques had reduced gray matter covariation in components including dorsal and ventral prefrontal cortex, superior temporal sulcus, and sylvian fissure [START_REF] Alexander | Age-related regional network of magnetic resonance imaging gray matter in the rhesus macaque[END_REF]. Previous studies in chimpanzees have also reported age-related changes in gray matter covariation, with decreased gray matter covariation reported for the superior frontal, supplementary motor, and anterior temporal cortex as chimpanzees age (Hopkins, W. D. et al., 2019). In addition, they reported increased gray matter covariation in the prefrontal and premotor cortices and part of the cerebellum as chimpanzees age (Hopkins, W. D. et al., 2019). A more recent study of agerelated changes in chimpanzee gray matter (using voxel-based morphometry) found significant reductions in the prefrontal cortex, middle temporal cortex, superior frontal cortex, insula, superior temporal cortex, and entorhinal cortex with increased age [START_REF] Mulholland | Age-and cognition-related differences in the gray matter volume of the chimpanzee brain (Pan troglodytes): A voxel-based morphometry and conjunction analysis[END_REF]. In the current study, we use source-based morphometry to examine the relationship between age and gray matter covariation in a baboon sample. We hypothesized that significant age effects would be found in one or more of the SBM components, specifically those comprised of regions known to be influenced by age in humans and other non-human primates, such as the middle temporal lobe, prefrontal cortex, and the cerebellum.

In addition, we examined sex differences in gray matter covariation. Studies of the baboon brain have indicated that sex may account for variation in overall brain volume and sulcal length [START_REF] Atkinson | Cortical folding of the primate brain: An interdisciplinary examination of the genetic architecture, modularity, and evolvability of a significant neurological trait in pedigreed baboons (Genus Papio)[END_REF][START_REF] Rogers | Heritability of brain volume, surface area and shape: An MRI study in an extended pedigree of baboons[END_REF], differences in the area of the corpus collosum [START_REF] Phillips | Tracking development of the corpus callosum in fetal and early postnatal baboons using magnetic resonance imaging[END_REF], and whole brain gray and white matter volumes [START_REF] Franke | Premature Brain Aging in Baboons Resulting from Moderate Fetal Undernutrition[END_REF]. [START_REF] Franke | Premature Brain Aging in Baboons Resulting from Moderate Fetal Undernutrition[END_REF] found that males had significantly higher absolute gray matter volume than females but did not examine regional differences or gray matter covariation. [START_REF] Atkinson | Cortical folding of the primate brain: An interdisciplinary examination of the genetic architecture, modularity, and evolvability of a significant neurological trait in pedigreed baboons (Genus Papio)[END_REF] reported that sex accounted for a significant proportion of variance in the length of the central sulcus, lateral fissure, lunate, and superior temporal sulcus. In chimpanzees, an SBM analysis revealed significant sex differences in gray matter covariation, including the cerebellum, primary visual, anterior temporal, and frontopolar cortices (Hopkins, W. D. et al., 2019). Similar to chimpanzees, here we hypothesized that there would be significant sex differences in one or more of the baboon SBM components.

Materials and Methods

Subjects

We utilized MRI data collected from 89 captive olive baboons (56 females, 31 males) from the Station de Primatologie CNRS in Rousset, France (Agreement number for conducting experiments on vertebrate animals: D13-087-7). The baboons ranged in age from 2 to 26 years of age (M = 11.46, SD = 5.89). All subjects were socially housed with free access to indoor/outdoor enclosures furnished with wooden platforms and vertical climbing structures. Care staff fed the baboons a diet of commercial primate chow, seed, and fresh produce four times per day. All baboons had ad libitum access to water.

All procedures complied with the current French laws and the European directive 86/609/CEE and were approved by the Provence Alpes Côte d'Azur ethics committee.

Image Acquisition and Post-Image Processing

Magnetic resonance image (MRI) scans were acquired using a 3.0 Tesla scanner (MEDSPEC 30/80 ADVANCE; Bruker) with a Rapid-Biomed surface antenna located at the Marseille Functional MRI Center. Detailed information about both image acquisition and post-image processing can be found in [START_REF] Love | The average baboon brain: MRI templates and tissue probability maps from 89 individuals[END_REF]. Briefly, each baboon was sedated with an intramuscular injection of ketamine (10 mg/kg) prior to transportation to the imaging facility. Upon arrival, subjects were further sedated with intramuscular injections of tiletamine and zolazepam (Zoletil™, 7 mg/kg) and acepromazine (Calmivet™, 0.2-0.5 mg/kg). During the imaging procedures, subjects were anesthesized with a drip irrigation of tiletamine, zolazepam (Zoletil™, 4 mg/kg/h) and NaCl (0.9% of 4 ml/kg/h). Veterinary staff monitored cardiovascular and respiratory functions with a SpO2 device and respiratory belt. Animals were placed into the scanner in a prone position with their head supported by cushions and Velcro strips. High resolution structural T1-weighted images were acquired (TR: 9.4 ms; TE: 4.3 ms; flip angle: 30°; inversion time: 800 ms); due to size field of view and isotropic voxel size differed for female and young male baboons (fov: 108 × 108 × 108 mm; isotropic voxel size: 0.6 mm3) and adult males (fov: 126 × 126 × 126 mm; isotropic voxel size: 0.7 mm3). Upon completion of the scan, subjects were monitored and fully recovered from anesthesia prior to being reunited with their social groups.

Resulting images were first denoised using an Adaptive Nonlocal Means filter (ANLM; [START_REF] Manjón | Adaptive non-local means denoising of MR images with spatially varying noise levels[END_REF]) before brain extraction using both Multi Atlas Skull Stripping software (http://www.cbica.upenn.edu/sbia/software/MASS/index.html; [START_REF] Doshi | Multi-atlas skull-stripping[END_REF][START_REF] Ou | DRAMMS: Deformable registration via attribute matching and mutual-saliency weighting[END_REF] and ITK-SNAP (version 3.2, www.itksnap.org, [START_REF] Yushkevich | User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability[END_REF]. Next, the skull stripped images were bias corrected using an N4 algorithm [START_REF] Tustison | N4ITK: improved N3 bias correction[END_REF]. Finally, we ran these images through the FSL-VBM pipeline using the FMRIB Software library tools (FSL; Oxford; http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLVBM). This process included (1) segmentation of each scan into gray and white matter, (2) linear registration of each scan to a standard baboon template [START_REF] Love | The average baboon brain: MRI templates and tissue probability maps from 89 individuals[END_REF], (3) creation of a study-specific gray matter template [START_REF] Andersson | Non-linear registration aka Spatial normalisation FMRIB Technial Report TR07JA2[END_REF][START_REF] Douaud | Anatomically related grey and white matter abnormalities in adolescent-onset schizophrenia[END_REF][START_REF] Smith | Advances in functional and structural MR image analysis and implementation as FSL[END_REF], (4) non-linear registration of each subject's gray matter image to the study-specific template, (5) modulation of the gray matter volume by use of a Jacobian warp to correct for local expansion or contraction of gray matter within each voxel, and (6) smoothing with an isotropic Gaussian kernel with a sigma of 2 mm. The resulting smooth, modulated gray matter volumes were used in subsequent analyses.

Statistical Analyses

Source-based morphometry. The data were analyzed using source-based morphometry (SBM v 1.0b) performed in the Group ICA of fMRI Toolbox (GIFT; http://icatb.sourceforge.net) (Xu et al., 2009a) in MATLAB R2015b. We imported the smoothed, modulated gray matter volumes for each subject and allowed the software to estimate the number of components based on an independent component analysis using a neural network algorithm. After estimation, the program calculates the parameters and creates a 4D volume containing each of the independent spatial components. At the individual level, SBM produces a weighted score that reflects each subject's relative contribution to the creation of each component, which were then used in subsequent statistical analyses. To examine the brain regions contributing to each component, we registered the component maps (scaled to standard deviation units and z scores) to the standard baboon template brain [START_REF] Love | The average baboon brain: MRI templates and tissue probability maps from 89 individuals[END_REF] and set the z-score threshold at |z| ≥ 3.00 as has been used in previous studies in humans and chimpanzees (see Hopkins, W. D. et al., 2019;Hopkins et al., 2020b;Xu et al., 2009a). All brain regions reaching this threshold were considered significant, and the volume was then measured using the region-of-interest tool in ANALYZE 14.

Age and sex effects. We ran a multivariate analysis of variance (MANOVA) with age group and sex (female, male) as the independent variables and weighted scores for each SBM component as dependent variables. Subsequent univariate ANOVAs were then used to examine differences between the age groups on their contribution to each individual SBM component.

Results

The SBM analysis identified 10 independent gray matter covariation components and corresponding individual weighted scores. The largest regions within each component included 1. cerebellum (bilateral), 2. caudate (bilateral) and putamen (bilateral), 3. brainstem (bilateral) and superior parietal cortex (bilateral), 4. isthmus of the cingulate (bilateral), 5. occipital cortex (bilateral), 6. brainstem (bilateral), 7. thalamus (bilateral), 8. middle frontal and superior frontal cortex (bilateral), 9. amygdala and hippocampus (bilateral), and 10. brainstem and cerebellum (bilateral). Full anatomical descriptions of each component and their respective gray matter volumes can be found in Table 1. 3D renderings of each component can be found in the supplemental materials. Baboons were grouped by age: juvenile (2-6 years old), young adult (7-12 years old), middle age

Discussion

Using source-based morphometry, we identified 10 components of gray matter covariation in the baboon brain and examined the relationship with age. First, contribution to components 3 and 8 decline as baboons age. Juvenile baboons contributed more to gray matter covariation components (compared to all other age classes) which include the middle frontal cortex, superior frontal cortex, brainstem, superior parietal cortex, thalamus and pallidum, precentral cortex, insula, and precuneus. In addition, we found adult baboons (of all age classes) contribute more to components 5 and 7 (occipital cortex, thalamus, putamen, and insula). The current findings expand on those reported by [START_REF] Franke | Premature Brain Aging in Baboons Resulting from Moderate Fetal Undernutrition[END_REF] who found reductions in overall gray matter volume as baboons age. Here we show that gray matter covariation within specific regions of the baboon brain change with increased age. As the baboons in the current study had no known illnesses or injuries, the changes in gray matter covariation here can presumably be attributed to normal aging, similar to neurological changes reported in healthy aging humans and chimpanzees (Hopkins, W. D. et al., 2019;[START_REF] Lemaitre | Normal age-related brain morphometric changes: nonuniformity across cortical thickness, surface area and gray matter volume?[END_REF][START_REF] Terribilli | Age-related gray matter volume changes in the brain during non-elderly adulthood[END_REF].

Further, it is possible that these regional changes are related to age-related changes in cognition. In chimpanzees, aged individuals show moderate loss in cognitive and motor functions compared to younger conspecifics (Hopkins et al., 2020a;Hopkins, W.D. et al., 2019;[START_REF] Hopkins | Genetic basis for motor skill and hand preference for tool use in chimpanzees (Pan troglodytes)[END_REF][START_REF] Lacreuse | Age-related decline in cognitive flexibility in female chimpanzees[END_REF][START_REF] Lacreuse | Cognitive and motor aging in female chimpanzees[END_REF], and age-related changes in cognition are related to changes in gray matter volume particularly within the frontal and parietal cortices, and precentral cortex [START_REF] Mulholland | Age-and cognition-related differences in the gray matter volume of the chimpanzee brain (Pan troglodytes): A voxel-based morphometry and conjunction analysis[END_REF]. In rhesus macaques, age-related declines in cognitive performance (i.e., delayed response, recognition memory, executive function) are related to decreases in overall volume, synaptic density, and receptor binding density of the dorsolateral prefrontal cortex [START_REF] Hara | Neuronal and morphological bases of cognitive decline in aged rhesus monkeys[END_REF]. Like humans, chimpanzees, and rhesus macaques, baboon cognition also appears to decline with age; performance on transfer index, novel task learning, precision, and simple discrimination tasks are worse in older adult compared to younger adult baboons [START_REF] Bonté | Age effects on transfer index performance and executive control in baboons (Papio papio)[END_REF][START_REF] Lizarraga | Age-related cognitive decline in baboons: modeling the prodromal phase of Alzheimer's disease and related dementias[END_REF]; however, additional studies are needed to directly test the relationship between these previously reported cognitive declines and the age-related neuroanatomical differences reported in the current study.

In addition, we found significant sex differences in gray matter covariation. Females contributed more to components which included the brainstem (bilateral), superior parietal cortex (bilateral), thalamus and pallidum (right), precuneus (left), middle and superior frontal cortices (bilateral), amygdala and hippocampus (bilateral), superior temporal cortex (bilateral), and rostral middle frontal (left). Whereas males contributed more to components including caudate and putamen (bilateral), cerebellum (bilateral), and lingual cortex (bilateral). These findings are consistent with other studies which show sex differences in baboon overall brain volume and sulcal length, corpus collosum area, and whole brain gray and white matter volumes [START_REF] Atkinson | Cortical folding of the primate brain: An interdisciplinary examination of the genetic architecture, modularity, and evolvability of a significant neurological trait in pedigreed baboons (Genus Papio)[END_REF][START_REF] Franke | Premature Brain Aging in Baboons Resulting from Moderate Fetal Undernutrition[END_REF][START_REF] Phillips | Tracking development of the corpus callosum in fetal and early postnatal baboons using magnetic resonance imaging[END_REF][START_REF] Rogers | Heritability of brain volume, surface area and shape: An MRI study in an extended pedigree of baboons[END_REF]. Baboons are sexually dimorphic in both anatomy and behavior.

Adult male baboons are larger than adult females, and also display different growth trajectories (with females reaching their adult size at earlier ages than males) and life expectancies [START_REF] Alberts | The malefemale health-survival paradox: a comparative perspective on sex differences in aging and mortality, Sociality, hierarchy, health: comparative biodemography: a collection of papers[END_REF][START_REF] Leigh | Growth and development of baboons, The baboon in biomedical research[END_REF][START_REF] Leigh | Sexual dimorphism in the baboon facial skeleton[END_REF][START_REF] Roseman | Genetic and environmental contributions to variation in baboon cranial morphology[END_REF]. Sex differences in baboon behavior are also apparent from infancy to adulthood (e.g. communication, social relationships, aggression, play, etc.) [START_REF] Bentley-Condit | Sex Differences in Captive Olive Baboon Behavior During the First Fourteen Days of Life[END_REF][START_REF] Brent | The study of captive baboon behavior[END_REF][START_REF] Chalmers | Developmental Relationships among Social, Manipulatory, Postural and Locomotor Behaviours in Olive Baboons, Papio anubis[END_REF][START_REF] Coelho | Sexual dimorphism in the activity of olive baboons (Papio cynocephalus anubis) housed in monosexual groups[END_REF][START_REF] Pereira | Effects of age and sex on intra-group spacing behaviour in juvenile savannah baboons, Papio cynocephalus cynocephalus[END_REF][START_REF] Smuts | Sex and Friendship in Baboons[END_REF][START_REF] Young | Gender and environment as determinants of behavior in infant common baboons (Papio cynocephalus)[END_REF][START_REF] Young | The development of grooming, sociosexual behavior, play and aggression in captive baboons in their first two years[END_REF]. As for cognition, there is limited evidence of sex differences to date. [START_REF] Lacreuse | Sex differences in inhibitory control in socially-housed baboons (Papio papio)[END_REF] found that males were slower but more accurate on a task of inhibitory control compared to females, perhaps due to testosterone mediated sensitivity to rewards. It is possible that the neuroanatomical sex differences reported in the current study could be related to these sex differences in behavior or cognition; however, more research is needed to examine these relationships.

Though the current study has many strengths, it is not without limitations. The sample was skewed toward younger and female baboons, with half the number of geriatric baboons (n=12) compared to other age classes (juvenile n=25, young adult n=30, middle-aged adult n=22), and almost twice as many females (n=58) as males (n=31). Although the proportions of males to females reflect proportions within many captive baboon colonies (baboons are typically housed in single-or two-male multi-female groups, i.e. harems), future studies should attempt to include data from more males and ensure that geriatric baboons are represented at numbers similar to other age classes. Despite these limitations, the current study shows that gray matter covariation differs between the two sexes and across age, with geriatric baboons contributing less to covariation components including the frontal and parietal cortices. Future research should examine the relationship between cognitive performance and both age-related and sex-related differences in gray matter covariation. We are very grateful to the Station de Primatologie CNRS, particularly the animal care staff and technicians, Jean-Noël Benoit, Jean-Christophe Marin, Valérie Moulin, Fidji and Richard Francioly, Laurence Boes, Célia Sarradin, Brigitte Rimbaud, Sebastien Guiol, Georges Di Grandi but also the vet Alice Bertello for their critical involvement in this project, Ivan Balansard, Sandrine Melot-Dusseau, Laura Desmis, Frederic Lombardo and Colette Pourpe for additional assistance.

Disclosure Statement

The authors have no conflicts of interest to report. Table 1. Regions and corresponding volumes (≥ 20 mm 3 ) of the 10 baboon SBM components.

Tables

Juveniles

Young Adults Middle Aged Adults Elderly

(

  Figure 2.

  16-CONV-0002 (ILCB) and from the Excellence Initiative of Aix-Marseille University (A*MIDEX). MRI acquisitions were done at the Center IRM-INT (UMR 7289, AMU-CNRS), platform member of France Life Imaging network (grant ANR-11-INBS-0006).

Figure 2 .-

 2 Figure 2. The average weighted SBM score on each SBM component for males and females. Males contributed more to component 2, whereas females contributed more to components 3, 8, and 9. * p < 0.05, ** p < 0.01

  The average weighted SBM score on Components 3, 5, 7, and 8 for each age class. Elderly baboons contributed more to Components 5 and 7 but contributed less to components 3 and 8 compared to juvenile baboons (p < 0.05).

		Figures
	Component Region	Volume	Left/Right Volume
	C1	cerebellum	3210.84	1678.75 / 1532.09
	C2	putamen	391.18	231.34 / 159.84
		cerebellum	368.07	241.06 / 127.01
		caudate	300.24	130.03 / 170.21
		lingual gyrus	217.3	131.33 / 85.97
		inferior temporal cortex	47.95	47.95 / 0
		brainstem	43.85	43.85 / 0
	C3	brainstem	478.88	299.81 / 179.07
		superior parietal	454.89	284.47 / 170.42
		thalamus & pallidum	424.01	2.81 / 421.20
		precuneus	53.35	44.28 / 9.07
		cerebellum	39.53	15.77 / 23.82
	C4	isthmus cingulate	203.26	92.88 / 110.38
		insula	98.71	0 / 98.71
		amygdala	66.74	0 / 66.74
		inferior parietal	63.72	39.96 / 23.76
		superior temporal cortex	45.15	0 / 45.15
	C5	occipital cortex	3843.08	2098.66 / 1744.42
	C6	brainstem	1877.68	1010.66 / 867.02
	C7	thalamus	1879.2	909.58 / 969.62
		putamen & insula	665.5	484.06 / 181.44
	C8	middle frontal cortex	1227.52	578.66 / 648.86
		superior frontal cortex	1104.63	559.01 / 545.62
		precentral cortex and insula	92.23	0 / 92.23
	C9	amygdala & hippocampus	2164.97	1295.57 / 869.40
		superior temporal cortex	518.19	309.53 / 208.66
		rostral middle frontal cortex	56.81	56.81 / 0
		pericalcarine	49.68	49.68 / 0
		precuneus	48.6	4.54 / 44.06
		brainstem	28.29	11.66 / 16.63
	C10 Figure 1.	brainstem & cerebellum	3570.05	2022.41 / 1547.64

Acknowledgements

AM has received funding from the European Research Council under the European Union's Horizon 2020 research and innovation program grant agreement No 716931 -GESTIMAGE -ERC-2016-STG, from the French "Agence Nationale de le Recherche" ANR-12-PDOC-0014-01 (LangPrimate), ANR-


SOIL MONITORING SYSTEMS

Challenges / recommendations towards harmonization

• Differences in sampling strategies, designs and protocols make soil data difficult to compare across countries and with LUCAS Soil • Countries do not want to change their protocols but could add new monitoring sites • Harmonization options of soil monitoring systems and LUCAS Soil exist, such as developing transfer functions • Major differences between a national soil monitoring system and LUCAS both on sampling strategy and measured soil properties can impact soil quality and soil health assessment

INTRODUCTION

Soils are constantly evolving due to natural processes driven by factors such as climate and soil organisms, but also due to external pressures linked mainly to human activities. The evolution of soils makes it necessary to set up monitoring programs.

Since 20 years, several projects and initiatives (e.g. ENVASSO,Landmark,SOIL4EU) underlined the existing difficulties to compare and share data between national Soil Monitoring Systems (SMS), either due to technical issues (e.g. different sampling designs and protocols, analytical methods, data format, standards) but also due to lack of motivation (e.g. why share the data?, costs) and legal requirements. These difficulties pushed EU-JRC to develop its own monitoring system (LUCAS Soil) to report on the status of soils in Europe.

How to combine the efforts of Member States in monitoring soils with the one developed by EU-JRC within the LUCAS Soil program?

A questionnaire was designed and circulated to EJP SOIL partners to identify main differences between SMS and possible ways of harmonization.

DIFFERENT SAMPLING STRATEGIES, DESIGNS AND PROTOCOLS BETWEEN SMS

A transversal analysis was made to identify similarities and differences between the 27 reported SMS (collected from 18 countries). Most SMS were developed and started in the nineties to monitor soil quality. Agriculture is the main land use investigated in our survey; forestry regularly has its own national SMS. The majority of SMS have at least 2 sampling campaigns (done or currently running) or more. The number of sites per country is highly variable but most have at least 1 site representing 300 km². In the majority of SMS, the monitoring sites were selected according to several criteria such as land use, soil type, main crop, climatic zone, but regular grids are also used. On monitored sites, 50 to 60% of the countries also collect information on soil management and on the surroundings. The sampling protocol is quite variable as the sampling area ranges from less than 5 m² to 1 ha. The depths of sampling are also quite different as samples are taken according to soil horizons or just at one depth (0-20 or 0-30 cm) or at multiple depths (2 to 5).

Mason E., Froger C., Bispo A., Fantappiè M., Hessel R., van Egmond F., Wetterlind J., Bożena S., Bakacsi Z., & Chenu C. DOI: 10.17180/RVXN-JA42

KEY MESSAGES FOR POLICY MAKERS Recommendation 1: Maintain existing long-established Soil Monitoring Systems with extensive data

Several countries developed SMS in the nineties. Even if highly diverse in sampling strategies and protocols, existing data from different periods are extremely valuable. As countries are not willing to change their SMS, the possibilities to combine results of SMS and LUCAS Soil campaigns should be developed.

Recommendation 2: Support the harmonization of Soil Monitoring Systems and LUCAS with more resources

Funding is needed to support countries to add new monitoring sites in their SMS that should be in common with LUCAS to allow a better comparison in the future and develop transfer functions (see Table 1).

Recommendation 3:

Improve the way data from Soil Monitoring Systems and LUCAS Soil are used Data from national SMS and LUCAS Soil campaigns are not easily usable together. Care should be taken when using the LUCAS dataset, as it may not be representative of all land covers and soil types. Depending on the country, national datasets from SMS should be preferred to conduct soil quality and soil health assessments. Therefore, more work is needed to exploit the benefits from both data sources, e.g. by developing transfer functions (see Table 1).

HARMONISATION WITH LUCAS SOIL

Considering harmonization with LUCAS Soil campaigns, apart from a few exceptions, the countries do not want to change their protocols (from the design to the analytical part). A majority of the countries would accept to add new monitoring sites (e.g. that could be in common with LUCAS) and are open to perform, with a proper budget, double sampling campaigns to account for differences in lab methods and an analysis to compare their results with LUCAS, which has now started with EJP SOIL budget. This will allow the development of validated transfer functions.

Several options towards harmonized SMS and LUCAS were discussed and are currently being tested within EJP SOIL WP6 (Table 1). The ambition is to improve the use of the existing data in EU countries and at EU level and also to benefit from the LUCAS campaigns to test harmonization options.

Advantages Limitations EJP SOIL progresses

Compare National and LUCAS datasets

Can identify the main differences and similarities between national and EU datasets Will require statistical skills to be tested and developed.

Test within EJP SOIL is ongoing and preliminary results are described in the paragraph below.

Develop transfer functions for data produced with national and LUCAS sampling protocols and/or analytical methods

Can improve the use of national and LUCAS datasets together.

Will require time and money for analysis and training (e.g. for sampling).

Tests within EJP SOIL are ongoing in collaboration with the LUCAS Soil programme. Taking the opportunity of LUCAS 2022 campaign, EJP SOIL partners are currently analysing soil samples collected by LUCAS samplers to compare several analytical methods. Some partners also sampled according to the LUCAS protocol in order to compare not only analytical methods but also sampling protocols. Results are expected in 2024.

Identify and test statistical methods to combine national and LUCAS datasets or maps

Will require statistical skills to develop and test appropriate methods.

Tests within EJP SOIL are ongoing, e.g. on data collected with different sampling strategies -included in EJP SOIL WP6 work programme.

Develop framework for interpretation approaches

Will require pedological and statistical skills to develop and test methods.

Tests within EJP SOIL are ongoing-included in EJP SOIL WP6 work programme and connected to SERENA project.

ZOOM IN ON ONE HARMONISATION OPTION: COMPARING NATIONAL AND LUCAS MONITORING STRATEGIES AND DATASETS

To explore the technical options for further harmonization with LUCAS, the LUCAS Soil program was compared in more detail with the SMS of 10 countries (BE, DE, DK, EE, FI, FR, HU, IT, SE, and SK), looking at monitoring strategies, soil types and properties distribution.

The results showed clear differences in monitoring strategies between LUCAS and national SMS. Most of the countries showed a higher number of sampling points in national SMS compared with LUCAS, up to 100 times higher for some countries (i.e. Belgium). Compared to national SMS, sampling in cropland was overrepresented in the LUCAS sampling at the expense of woodlands.

Finally, LUCAS oversampled and/or undersampled some soil types compared with the national SMS which better represented the soil type distribution in the countries. Sampling strategies (e.g. stratified random sampling or grid sampling) were different for several SMS and LUCAS. As LUCAS Soil campaigns were designed for specific purposes at EU scale (e.g. focus on cropland), statistical distributions at country level may be biased. Results from soil properties measured by LUCAS and by national SMS were also compared (e.g. organic carbon, clay content and pH). Significant differences were observed depending on land cover (Figure 1). Consequently, an under or overrepresentation of land covers and soil types in the monitoring program can affect the reported results of soil health indicators, such as those suggested in the proposed Soil Monitoring Directive. This underlines the importance of a welldesigned SMS and that reference and threshold values to evaluate soil health should be selected with care.

Differences exist between each national soil monitoring system and LUCAS Soil on soil properties

In order to harmonize national SMS with LUCAS datasets, it is necessary to develop transfer functions for data collected with different laboratory methods, sampling protocols, for sampling design differences, etc. Additionally, the findings highlight the importance of close collaboration between LUCAS Soil and National SMS to identify any gaps in soil information (such as for certain soil types and land covers) and ensure that the necessary steps are taken to allow harmonisation of the results of monitoring programs if desirable. Bispo, A., Arrouays, D., Saby, N., Boulonne, L., & Fantappiè M. (2021) The report can be found on our webpage: https://ejpsoil.eu/

REFERENCE TO THE REPORT

Table 1 .

 1 Analysis of the harmonisation's options

National SMS sampling campaign in France © Claudy Jolivet


Valentin Leducq
email: valentin.leducq@sorbonne-universite.fr.

Karen Zafilaza

Antoine Fauchois

Emna Ghidaoui

Sophie Sayon

Céline Dorival

Marie-Laure Meledje

Clovis Lusivika-Nzinga

Youri Yordanov

Guillaume Martin-Blondel

Fabrice Carrat

Anne-Geneviève Marcelin

Cathia Soulie

Spike protein genetic evolution in patients at high-risk of severe COVID-19 treated by monoclonal antibodies

Keywords: SARS-CoV-2, Monoclonal antibodies, COVID-19, Immunocompromised, Resistance mutations

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

BACKGROUND

Neutralizing mAbs target the Spike (S) glycoprotein on the surface of SARS-CoV-2, which mediate its entry into host cells via the hACE2 receptor [START_REF] Huang | Structure-based neutralizing mechanisms for SARS-CoV-2 antibodies[END_REF]. Several mAb therapies have been approved by the European Medicines Agency (EMA) since March 2021, including Casirivimab/Imdevimab, Sotrovimab, and Tixagevimab/Cilgavimab, for prevention and/or therapy of patients at high risk for progression to severe COVID-19, particularly immunocompromised patients who failed to respond to vaccine [START_REF] Ema | COVID-19 medicines[END_REF].

These antibodies target the receptor-binding domain (RBD) of the Spike protein in order to inhibit viral entry and can be classified into 4 distinct groups according to their structures and target epitopes [START_REF] Chen | Broadly neutralizing antibodies to SARS-CoV-2 and other human coronaviruses[END_REF][START_REF] Barnes | SARS-CoV-2 neutralizing antibody structures inform therapeutic strategies[END_REF]. Casirivimab is a class 1 antibody whose epitope overlaps with the receptor biding motif (RBM) within the RBD and compete with binding of hACE2 host receptor [START_REF] Mader | Omicron's binding to sotrovimab, casirivimab, imdevimab, CR3022, and sera from previously infected or vaccinated individuals[END_REF]. Imdevimab is a class 3 antibody that binds outside the RBM but close enough to hinder hACE2 interaction. Casirivimab/Imdevimab combination has demonstrated in vitro efficacy against Alpha and Delta variants, and also significantly reduces viral load in patients, as well as the risk of hospitalization and death [START_REF] Weinreich | REGN-COV2, a Neutralizing Antibody Cocktail, in Outpatients with Covid-19[END_REF][START_REF] Weinreich | REGEN-COV Antibody Combination and Outcomes in Outpatients with Covid-19[END_REF]. However, due to the large number of mutations in its Spike protein, these two antibodies became ineffective against Omicron variant, notably due to substitutions K417N, S477N, E484A, Q493R and G446S [START_REF] Mccallum | Structural basis of SARS-CoV-2 Omicron immune evasion and receptor engagement[END_REF].

Sotrovimab is a class 3 antibody that binds outside the RBM to a highly conserved epitope in sarbecoviruses and does not compete with binding of hACE2 [START_REF] Mader | Omicron's binding to sotrovimab, casirivimab, imdevimab, CR3022, and sera from previously infected or vaccinated individuals[END_REF]. Sotrovimab has shown in vitro efficacy against Delta and, unlike Casirivimab/Imdevimab, retains activity, although reduced, against Omicron [START_REF] Planas | Considerable escape of SARS-CoV-2 Omicron to antibody neutralization[END_REF]. In high-risk patients infected with Omicron, Sotrovimab effectively protects against progression to severe COVID-19 [START_REF] Martin-Blondel | Outcome of very high-risk patients treated by Sotrovimab for mild-to-moderate COVID-19 Omicron, a prospective cohort study (the ANRS 0003S COCOPREV study)[END_REF]. Sotrovimab became ineffective with the emergence of BA.2 sub-variant displaying additional mutations in the Spike protein [START_REF] Iketani | Antibody evasion properties of SARS-CoV-2 Omicron sublineages[END_REF]. However, recent studies suggest that Sotrovimab may retain activity against Omicron sub-variants, notably BQ.1.1 and XXB.1.5, due to the antibody-dependent cell-mediated cytotoxicity (ADCC) of its crystallizable fragment (Fc) region [START_REF] Bruel | Antiviral activities of sotrovimab against BQ.1.1 and XBB.1.5 in sera of treated patients[END_REF]. Tixagevimab and Cilgavimab are class 1 and 2 antibodies, respectively, whose epitopes overlap the RBM and compete with binding of hACE2 [START_REF] Planas | Considerable escape of SARS-CoV-2 Omicron to antibody neutralization[END_REF]. This combination has been shown to be effective in preventing severe COVID-19, especially as pre-exposure prophylaxis for high-risk individuals [START_REF] Soeroto | Efficacy and safety of tixagevimabcilgavimab as pre-exposure prophylaxis for COVID-19: A systematic review and metaanalysis[END_REF]. Tixagevimab/Cilgavimab was one of the last antibody therapies to maintain significant efficacy against BA.2 and BA.5, essentially due to Cilgavimab, until the emergence of the BQ.1 and XBB sub-variants [START_REF] Bruel | Longitudinal analysis of serum neutralization of SARS-CoV-2 Omicron BA.2, BA.4, and BA.5 in patients receiving monoclonal antibodies[END_REF][START_REF] Takashita | Efficacy of Antibodies and Antiviral Drugs against Omicron BA.2.12.1, BA.4, and BA.5 Subvariants[END_REF][START_REF] Cao | Imprinted SARS-CoV-2 humoral immunity induces convergent Omicron RBD evolution[END_REF].

While their efficacy and susceptibility to the various emerging variants of SARS-CoV-2 over time have been widely documented, concerns remain over the use of such antibody therapies in the development of resistance mutations and their impact on the genetic evolution of SARS-CoV-2. Our study aims to analyse the impact of these three mAb therapies on the genetic evolution of the S gene in high-risk patients by employing next-generation sequencing to detect viral population changes and minority variants emergence.

METHODS

Study design

Our study is based on the ongoing ANRS 0003S COCOPREV Study (NCT04885452) [START_REF] Martin-Blondel | Outcome of very high-risk patients treated by Sotrovimab for mild-to-moderate COVID-19 Omicron, a prospective cohort study (the ANRS 0003S COCOPREV study)[END_REF]

SARS-CoV-2 viral RNA load

Viral RNA was isolated from nasopharyngeal swab stored in universal transport medium on Nuclisens® Easymag™ (Biomérieux), with a starting volume of 300µl eluted in 70µl. Cycle threshold (Ct) values were estimated using the TaqPath™ COVID-19 RT-PCR kit (ThermoFisher). The SARS-CoV-2 ORF1ab, N and S genes were simultaneously amplified to generate cycle thresholds (Cts), which were then converted into viral copies per milliliter of sample (cp/ml) using a standard curve developed in our laboratory with standard samples quantified by droplet digital PCR (ddPCR).

Whole genome sequencing

Patients with both an initial sample (Day 0) and at least one follow-up sample with Ct<31 were included. Whole SARS-CoV-2 genome sequencing was performed according to the Oxford Nanopore "PCR tiling of SARS-CoV-2 virus Eco protocol". Viral RNA was reverse transcribed and amplified by PCR with the ARTIC primer pool v4.1 (Integrated DNA Technologies). Samples were basecalled with super-accurate option and demultiplexed with GUPPY (v6+). Reads were mapped to the Wuhan-hu-1 (MN908947.3) reference genome with minimap2 (v2.24), and consensus were generated with BCFTools (v1.16). Clades and lineages were assigned with Nextclade (v2+) and Pangolin (v4+). Single nucleotide variations (SNVs) calling was performed with PEPPER-Margin-DeepVariant pipeline (v0.8) [START_REF] Shafin | Haplotype-aware variant calling with PEPPER-Margin-DeepVariant enables high accuracy in nanopore longreads[END_REF].

Statistical analysis

We used multivariable logistic regression to identify variables associated with S protein substitution emergence. Initial univariate analyses were conducted, selecting variables with a P-Value <0.3 for subsequent multivariate analysis. To address interrelated categorical variables, we developed four distinct models, prioritizing the most robust ones. A significance threshold of 5% was applied for establishing associations. We assessed the impact of variant, treatment, and mutation emergence on SARS-CoV-2 viral RNA loads during follow-up. We employed the Kruskal-Wallis test and Wilcoxon rank sum tests for pairwise comparisons to identify significant categories influencing viral RNA load. All statistical analyses were carried out using R, and univariate comparison tests were two-tailed.

General population

We extracted 65,448 high-coverage SARS-CoV-2 genomes from France between August 2021 and December 2022 from GISAID database. Genomes were aligned using MAFFT (v7.475), and we estimated intra-populational frequencies of substitutions correlated with Casirivimab/Imdevimab, Sotrovimab, or Tixagevimab/Cilgavimab treatments over this period.

RESULTS

Patients description

Among the 264 patients analysed, 74 (28%) received Casirivimab/Imdevimab, 166 (63%) received Sotrovimab and 24 (9%) received Tixagevimab/Cilgavimab. All had mild-tomoderate COVID-19, and risk factors for severe COVID-19, including being ≥65 years-old (121/264, 46%) and/or being immunocompromised (207/264, 78%). Differences were observed between patients who received Casirivimab/Imdevimab, Sotrovimab or Tixagevimab/Cilgavimab as regards sex, hospitalisation, vaccination, certain types of immunodepression or comorbidities and treatment by pre-exposure prophylaxis with mAbs (Table 1). Patients treated with Casirivimab/Imdevimab had lower IgG anti-S levels at baseline (median 7 BAU/ml, 0-96 IQR) than patients treated by Sotrovimab (median 48 BAU/ml, 0-809 IQR, p=0.017) or Tixagevimab/Cilgavimab (median 359 BAU/ml, 56-638 IQR p<0.001). Delta infected patients received exclusively Casirivimab/Imdevimab (42/42, 100%), whereas BA.1/BA.2 and BA.5/BQ.1 infected patients received mainly Sotrovimab (166/182, 91%) and Tixagevimab/Cilgavimab (21/21, 100%), respectively. At baseline, no significant differences in SARS-CoV-2 viral RNA loads could be found between patients treated with these three mAbs therapies (p=0.974). Patients treated with Casirivimab/Imdevimab had lower viral RNA loads at day 7 (median 5.58, 4.96-6.41 IQR) than patients who received Sotrovimab (median 6.81, 6.07-7.64 IQR, p<0.001) or Tixagevimab/Cilgavimab (median 6.52, 5.81-7.66 IQR, p=0.022), and day 14 (median 3.53, 2.97-4.11 IQR) than patients treated with Sotrovimab (median 5.50, 3.88-6.85 IQR, p<0.001).

Emerging mutational profiles

Following the SARS-CoV-2 genome sequencing and SNVs calling, we have listed missense mutations who were absent at baseline but emerged in the S gene in patients' follow-up samples. Overall, amino acid substitutions occurred at 55 distinct residues in S protein under treatment with mAbs. Among these, 5 residues in RBD and 1 residue in HR2 domain were subject to amino acid substitutions with a >5% prevalence in patients (Figure 1a). Among patients treated with Casirivimab/Imdevimab, 27% (20/74) had a L1186F amino acid substitution in HR2 domain in S2 subunit. Among patients who received Sotrovimab, 28% (47/166), 13% (21/166) and 10% (17/166) had E340D/K/A/Q/V/G, K356T/R and P337S/R/L/H amino acid substitutions, respectively. These 3 substitution sites are located within the RBD but outside the RBM. Among patients treated with Tixagevimab/Cilgavimab, 68% (16/24) and 21% (5/24) had R346K/I/T/S and K444R/N/M amino acid substitutions, respectively. These substitutions are located in the RBD, outside the RBM for R346 and within the RBM loop for K444. For downstream analysis, we chose to only consider the six above-mentioned amino acid substitution sites in RBD and HR2 domain with highest prevalence (Figure 1b). Tixagevimab/Cilgavimab was associated with a reduced median time to, and increased probability of, mutation emergence in S protein compared to Casirivimab/Imdevimab and Sotrovimab (p<0.001) (Figure 1c). Intra-host frequencies of missense mutations selected in patients who received Casirivimab/Imdevimab appeared to be lower (median 20%, 15%-24% IQR) compared to intra-host frequencies of mutation selected in patients treated with Sotrovimab (median 70%, 48%-91% IQR, p<0.001) or Tixagevimab/Cilgavimab (median 57%, 51%-71% IQR, p<0.001) (Figure 1d).

Factors associated with the emergence of mutation

According to logistic regression models, immunodepression was associated with a 5.6-fold increased risk of emergence of considered mutations in S protein, regardless of sex, age, level of IgG anti-S, variant and mAbs treatment (p=0.019, OR 5.64, 95%CI: 1.34-23.81) (Supplementary figure 1). Patients treated with immunosuppressive therapy (including Rituximab and corticosteroids) have a 3-fold increased risk of mutation emergence (OR 2.94, 95%CI 1.03-8.37, p=0.044) and solid organ transplant recipients also tend to have these mutations emerge more frequently (OR 2.97, 95%CI 0.87-10.2, p=0.083), compared to nonimmunocompromised patients. Interestingly, patients who received Tixagevimab/Cilgavimab had a 5.3-fold increased risk of mutation emergence in S protein compared to patients treated with Casirivimab/Imdevimab (p=0.025, OR 5.28, 95%CI: 1.24-22.5), while patients who received Sotrovimab had a similar risk (p=0.726, OR 1.17, 95%CI: 0.49-2.80). Among patients who received Sotrovimab, the emergence of mutation in S protein was associated with increased viral RNA loads at day 7 (median 7.31, 6.55-7.98 IQR) and day 14 (median 6.32, 4.87-7.05 IQR), compared to patients with no mutation emergence at day 7 (median 6.5, 5.92-7.22 IQR, p<0.001) and at day 14 (median 4.25, 3.17-5.79 IQR, p<0.001) (Figure 2b).

The emergence of mutations in patients who received Tixagevimab/Cilgavimab seemed to be associated with higher viral RNA loads at day 14, but this difference was not significant (p=0.373) due to the small number of patients in this group (Figure 2c).

Overall, the emergence of such mutations was correlated with a longer median time to a viral RNA load ≤4.41 log 10 cp/ml (≥31Ct) (p<0.0001), regardless of mAbs treatment (Figure 3). Concerning hospitalisation at day 28 of follow-up, 4/105 (4%) patients with an emerging mutation in S protein and 7/159 (4%) patients without mutation emergence were hospitalised. Similarly, no significant difference was observed in the evolution of patients' symptoms, whether or not they developed a mutation in S protein during their follow-up (Supplementary material).

Emerging mutations in the general population

Amino acid substitutions of L1186, E340, K356 and P337 residues, associated with Casirivimab/Imdevimab and Sotrovimab therapies in the study, had low frequency levels (<1%) in the French general population, between August 2021 and December 2022 (data not showed). In contrast, amino acid substitution at the R346 site in the S protein, associated with Tixagevimab/Cilgavimab therapy, first emerged in the general population in December 2021, concomitantly with BA.1 variant (Figure 4). By the end of January 2022, R346 substitution has reached a frequency of 55% in the general population and eventually disappeared in March when BA.1 had been replaced by BA.2 variant. Finally, the R346 substitution emerged a second time in August 2022, in association with the K444 substitution, concomitantly with BQ.1 variant. By December 2022, these two substitutions have reached a frequency of 84% and 85% in the general population, respectively.

DISCUSSION

Overall, we reported six sites of amino acid substitutions in the Spike protein during followup of 264 high-risk patients treated with three different monoclonal antibody therapies. We assessed the impact of these substitutions on patient outcome and on the evolution of SARS-CoV-2.

Immunodepression is associated with an almost 6-fold increased risk of developing a mutation in S protein following mAb treatment, especially immunosuppression related to the use of immunosuppressive therapies (including rituximab and corticosteroids) and solid organ transplantation. Since the majority of the patients studied here are immunocompromised, it is difficult to establish a clear association between immune status and the acquisition of mutations. A non-immunocompromised control group of comparable size would enable definitive conclusions to be drawn. Regardless of patients' immune status and the mAbs administered, mutation emergence is associated with prolonged SARS-CoV-2 infection, increased viral RNA load levels, and delayed viral clearance. However, these mutations do not appear to affect patients' clinical progress or overall recovery. Although these mutations probably have an impact on the efficacy of mAbs, particularly those affecting the RBD, the three therapies still appear to be sufficiently active to prevent severe forms of COVID-19 in high-risk patients.

Casirivimab/Imdevimab is associated with the emergence of L1186F substitution in Deltainfected patients. This residue is conserved across coronaviruses within an E-L-L motif in the HR2 domain in S2 subunit that play a crucial role in fusion-mediated viral entry [START_REF] Jana | Targeting an evolutionarily conserved 'E-L-L' motif in the spike protein to develop a small molecule fusion inhibitor against SARS-CoV-2[END_REF]. There are no existing data showing that L1186F substitution may emerge with Casirivimab/Imdevimab or that it could confer resistance to these mAbs. Besides, Casirivimab (Class 1) binds to the RBM loop region (residues 471-491) and Imdevimab (Class 3) binds outside the RBM in RBD lower left edge [START_REF] Ghotloo | Epitope mapping of neutralising anti-SARS-CoV-2 monoclonal antibodies: Implications for immunotherapy and vaccine design[END_REF]. Together with low intra-host frequencies observed in patients and its absence in the general population, this information suggested that selection of L1186F substitution at individual and populational levels is unlikely, and provided no evolutionary or immune escape benefits. Thus, we had no explanation for the L1186F substitution emergence found exclusively in a subset of patients who received Casirivimab/Imdevimab. The rapid drop in viral RNA loads in these patients tended to confirm that L1186F substitution confers no resistance to Casirivimab/Imdevimab. Overall, our results suggest that Casirivimab/Imdevimab combination is highly effective against Delta, sufficiently to prevent the emergence of resistance mutations. Two studies also showed that the Casirivimab/Imdevimab combination effectively prevented mutation selection, both in vitro on VSV-based pseudotyped viruses displaying the original Wuhan-hu-1 Spike and in vivo in patients infected with Alpha, Delta and other pre-Omicron variants of concerns (VOCs) [START_REF] Copin | The monoclonal antibody combination REGEN-COV protects against SARS-CoV-2 mutational escape in preclinical and human studies[END_REF][START_REF] Baum | Antibody cocktail to SARS-CoV-2 spike protein prevents rapid mutational escape seen with individual antibodies[END_REF]. Nevertheless, a third recent study reveals the selection of several mutations, all within the RBM, at residues E406, G446, Y453 and L455 in a small number of Delta-infected patients treated with Casirivimab/Imdevimab [START_REF] Ragonnet-Cronin | Generation of SARS-CoV-2 escape mutations by monoclonal antibody therapy[END_REF]. In our study, we found only one patient with emerging G446V and one patient with emerging G446V+Y453F, both Delta-infected.

Emergence of P337 and E340 substitutions in RBD has been demonstrated several times in Delta-and Omicron-infected patients who received Sotrovimab and is associated with a 27to 279-fold reduction in SARS-CoV-2 susceptibility to this mAb [START_REF] Rockett | Resistance Mutations in SARS-CoV-2 Delta Variant after Sotrovimab Use[END_REF][START_REF] Cameroni | Broadly neutralizing antibodies overcome SARS-CoV-2 Omicron antigenic shift[END_REF][START_REF] Vellas | Resistance mutations in SARS-CoV-2 omicron variant in patients treated with sotrovimab[END_REF]. In a recent study, the emergence of the K356T mutation was also observed in two Omicron-infected patients treated with Sotrovimab [START_REF] Andrés | Emergence of Delta and Omicron variants carrying resistance-associated mutations in immunocompromised patients undergoing sotrovimab treatment with long-term viral excretion[END_REF]. A report from September 06, 2021 from the Japanese Ministry of Health alerts us that a 5.9-fold decrease in neutralization activity by Sotrovimab was estimated for pseudovirus particles carrying K356T and D614G mutations. In our study, 68/166 (41%) of patients treated with Sotrovimab acquired at least one of these three substitutions. Higher viral RNA loads during the follow-up of these patients are consistent with a decreased susceptibility to Sotrovimab induced by P337, E340 and K356 substitutions. These three substitutions are located at the Sotrovimab epitope covering amino acids 337-344 and 356-361 outside the RBM [START_REF] Pinto | Crossneutralization of SARS-CoV-2 by a human monoclonal SARS-CoV antibody[END_REF]. Interestingly, K356 substitution could be associated with the acquisition of an additional glycosylation site that could mask the antigenic epitope [START_REF] Lin | Glycan Masking of Epitopes in the NTD and RBD of the Spike Protein Elicits Broadly Neutralizing Antibodies Against SARS-CoV-2 Variants[END_REF]. Other than this resistance to Sotrovimab, there are no data to suggest that these substitutions are associated with increased transmissibility or any phenotypic change. Furthermore, we did not find any of these three mutations in the general population or in any of the SARS-CoV-2 VOCs. Tixagevimab (Class 1) and Cilgavimab (Class 2) bind to two non-overlapping epitopes covering the RBM. More precisely, Tixagevimab epitope overlaps the RBM ridge (residues 471-491), while Cilgavimab epitope overlaps the RBM loop (residues 443-450). Tixagevimab lost all efficacy with the advent of Omicron, partly due to the emergence of the Q493R and Q498R mutations, while Casirivimab demonstrated greater resilience, retaining reduced activity on the BA.2 and BA.5 sub-variants [START_REF] Focosi | A Critical Analysis of the Use of Cilgavimab plus Tixagevimab Monoclonal Antibody Cocktail (Evusheld TM ) for COVID-19 Prophylaxis and Treatment[END_REF]. Emergence of R346 or K444 substitutions were previously demonstrated both in vitro and in patients infected with BA.2 who received Tixagevimab/Cilgavimab [START_REF] Vellas | Resistance mutations in SARS-CoV-2 omicron variant after tixagevimab-cilgavimab treatment[END_REF][START_REF] Keam | Tixagevimab + Cilgavimab: First Approval[END_REF]. Interestingly, both substitutions have already been found in several SARS-CoV-2 lineages. First, R346K substitution (previously seen only in B.1.621 [START_REF] Fratev | R346K Mutation in the Mu Variant of SARS-CoV-2 Alters the Interactions with Monoclonal Antibodies from Class 2: A Free Energy Perturbation Study[END_REF]) occurred in the BA.1.1 sub-lineage of Omicron and is associated with a higher affinity for hACE2 and a 5-to 10-fold reduction in Cilgavimab activity [START_REF] Liu | Striking antibody evasion manifested by the Omicron variant of SARS-CoV-2[END_REF][START_REF] Cui | Structural and functional characterizations of infectivity and immune evasion of SARS-CoV-2 Omicron[END_REF][START_REF] Li | Structural basis of human ACE2 higher binding affinity to currently circulating Omicron SARS-CoV-2 sub-variants BA[END_REF][START_REF] Ortega | Mutations in the SARS-CoV-2 spike protein modulate the virus affinity to the human ACE2 receptor, an in silico analysis[END_REF]. While these two mAbs were ineffective against BA.1, with BA.2 and the disappearance of R346K, Cilgavimab regains efficacy while Tixagevimab remains ineffective [START_REF] Tao | Susceptibility of SARS-CoV-2 Omicron Variants to Therapeutic Monoclonal Antibodies: Systematic Review and Meta-analysis[END_REF][START_REF] Bruel | Serum neutralization of SARS-CoV-2 Omicron sublineages BA.1 and BA.2 in patients receiving monoclonal antibodies[END_REF]. Shortly thereafter, a multitude of Omicron sub-lineages display substitutions of the R346 residue, such as BA.4.6, BF.7, BA.5.2.6, BA.4.1.9, and BE.1.2 harbouring R346T ; BA.4.7 and BA.5.2.1 harbouring R346S ; and BA.5.9 with R346I. These sub-lineages with R346X mutations are completely resistant to Cilgavimab and induce a 2.4 to 2.6 fold reduction in plasma neutralizing activity from BA.5 infection [START_REF] Jian | Further humoral immunity evasion of emerging SARS-CoV-2 BA.4 and BA.5 subvariants[END_REF][START_REF] Liu | Identification of SARS-CoV-2 spike mutations that attenuate monoclonal and serum antibody neutralization[END_REF]. Spike K444 substitutions have been found in several Omicron sub-lineages, such as BA.4.6.3 (K444N), BA.2.3.20 (K444R) or BA.5.2.7 (K444M). Furthermore, Ortega et al.

showed that K444N/R mutations within the RBM loop were identified as stabilizing hACE2-Spike interaction and increasing the affinity for hACE2 [START_REF] Ortega | Mutations in the SARS-CoV-2 spike protein modulate the virus affinity to the human ACE2 receptor, an in silico analysis[END_REF]. Together, R346 and K444 substitutions are part of a convergent evolution of RBD in a multitude of Omicron sublineages with growth advantages over BA.5 [START_REF] Focosi | Convergent Evolution in SARS-CoV-2 Spike Creates a Variant Soup from Which New COVID-19 Waves Emerge[END_REF]. Mutations R346T and K444T have now become widespread in the general population as they are found in the BQ.1.1 and XBB.1.5 variants which became dominant in late 2022.

Interestingly, the risk of emergence of mutations was 5-fold higher in patients treated with Tixagevimab/Cilgavimab. Within this bi-therapy, only Cilgavimab retained activity on post-BA.1 variants and was finally used as a monotherapy [START_REF] Bruel | Serum neutralization of SARS-CoV-2 Omicron sublineages BA.1 and BA.2 in patients receiving monoclonal antibodies[END_REF]. However, bi-therapy with Casirivimab/Imdevimab has shown greater efficacy against the Delta variant, while recent studies have shown that Sotrovimab may retain efficacy thanks to its Fc-effector functions, even with virus partially resistant to neutralization, unlike the other two mAb combinations [START_REF] Bruel | Antiviral activities of sotrovimab against BQ.1.1 and XBB.1.5 in sera of treated patients[END_REF][START_REF] Martin-Blondel | Sotrovimab to prevent severe COVID-19 in high-risk patients infected with Omicron BA.2[END_REF][START_REF] Roe | Molecular Characterization of AZD7442 (Tixagevimab-Cilgavimab) Neutralization of SARS-CoV-2 Omicron Subvariants[END_REF]. Together, these data suggest that mAb monotherapy, without Fc-effector functions, is highly sensitive to the emergence of mutations located in the targeted epitope reducing neutralizing activity and may explain the higher risk of mutation emergence with Tixagevimab/Cilgavimab.

In conclusion, our analysis highlights how using mAbs to treat high-risk COVID-19 patients can drive genetic evolution of SARS-CoV-2, potentially leading to treatment resistance through the rapid and frequent acquisition of mutations in Spike protein in immunocompromised patients. To mitigate this risk, our findings suggest that employing bitherapies and mAbs featuring Fc-effector functions may be beneficial. Moreover, we have identified these resistance mutations across multiple SARS-CoV-2 lineages, including various VOCs, emphasizing the need to assess the impact of mAb treatments on SARS-CoV-2 evolution more broadly within the population.

  , a multicentric prospective cohort enrolling PCR-confirmed mild-to-moderate COVID-19 patients at high risk of severe progression. Treatment was administered within the initial five days of symptom onset under emergency use authorization or early access at one of 32 participating centers. Patients received either 600/600 mg or 300/300 mg of Casirivimab/Imdevimab IV, 500 mg of Sotrovimab IV, or 300/300 mg of Tixagevimab/Cilgavimab IV, following French health authority guidelines and physician discretion. Nasopharyngeal swabs were collected on Day 0 and 7, with additional tests on Day 3 and 5 for hospitalized patients, and subsequently weekly while viral RNA loads remained positive.

Figure 1 :

 1 Figure 1: Mutational profiles and kinetics under mAbs therapies.

Figure 2 :

 2 Figure 2: Viral load evolution over time depending on resistance mutation acquisition.

Figure 3 :

 3 Figure 3: Viral load persistence over time depending on resistance mutation acquisition.

Figure 4 :

 4 Figure 4: Frequencies of Tixagevimab/Cilgavimab induced substitutions in the general population.

ACKNOWLEDGMENTS

This study has been labelled as a National Research Priority by the National Orientation Committee for Therapeutic Trials and other researches on Covid-19 (CAPNET). The investigators would like to acknowledge ANRS | Emerging infectious diseases for their scientific support, the French Ministry of Health and Prevention and the French Ministry of Higher Education, Research and Innovation for their funding and support.

FUNDING

The ANRS0003S COCOPREV cohort is conducted with the support of ANRS│MIE and funded by French Ministries « Ministère des Solidarités et de la Santé and Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation ».

ETHICAL APPROVAL

This

study received the ethical approval of the « Comité de Protection des Personnes (SUD-EST IV) ». This study would have not been possible without the teams involved in the COCOPREV Study and designated as the CO-COPREV Study Group: Magali Garcia, Valentin Giraud, Agathe Metais, France Cazenave-Roblot, Jean-Philippe Martellosio (CHU de Poitiers); Anne-Marie Ronchetti, Thomas Gabas, Naima Had-jadj, Célia Salanoubat, Amélie Chabrol, Pierre Housset, Agathe Par-don, Anne-Laure Faucon, Valérie Caudwell, Latifa Hanafi (CHU Sud Francilien, Corbeil-Essonne); Laurent Alric, Grégory Pugnet, Mor-gane Mourguet, Eva Bories, Delphine Bonnet, Sandrine Charpentier, Pierre Delobel, Alexa Debard, Colleen Beck, Xavier Boumaza, Stella Rousset (CHU de Toulouse); Fanny Lanternier, Claire Delage, Elisabete Gomes Pires, Morgane Cheminant, Nathalie Chavarot (Hôpital Necker, Paris); Anthony Chauvin, Xavier Eyer; Véronique Delcey (Hôpital Lariboisière, Paris); Simon Bessis, Romain Gueneau (Hôpital du Kremlin Bicêtre); Pelagie Thibaut, Marine Nadal, Mar-tin Siguier, Marwa Bachir, Christia Palacios (Hôpital Tenon, Paris); Valérie Pourcher, Antoine Faycal, Vincent Berot, Cécile Brin, Siham Djebara, Karen Zafilaza, Stephane Marot, Sophie Sayon, Valentin Leducq (Hôpital de la Pitié Salpêtrière,Paris); Karine Lacombe, Yasmine Abi Aad, Thibault Chiarabini, Raynald Feliho, Nadia Valin, Fabien Brigant, Julien Boize, Pierre-Clément Thiébaud, Marie Moreau, Charlotte Billard (Hôpital St Antoine, Paris), Nathalie De Castro, Geoffroy Liégeon, Blandine Denis, Jean-Michel Molina, Lucia Etheve (Hôpital Saint Louis, Paris); André Cabié, Sylvie Abel, Ornella Cabras, Karine Guitteaud, Sandrine Pierre-François (CHU de Martinique); Vincent Dubee, Diama Ndiaye, Jonathan Pehlivan, Michael Phelippeau, Rafael Mahieu (CHU d'Angers); Charles Cazanave (CHU de Bordeaux); Alexandre Duvignaud, Thierry Pistone, Arnaud Desclaux, Didier Neau, Jean-François Faucher, Benjamin Festou, Magali Dupuy-Grasset, Véronique Loustaud-Ratti, Delphine Chainier (CHU de Limoges); Nathan Peiffer-Smadja, Olivia Da Conceicao, Michael Thy, Lio Collas, Cindy Godard, Donia Bouzid, Vittiaroat Ing, Laurent Pereira, Thomas Pavlowsky, Camille Ravaut (Hôpital Bichat,

CONFLICT OF INTEREST STATEMENT

All authors declare no conflicts of interest.

Supplementary figure 1: Results of selected logistic regression models.

Model #1 looks for associations between the emergence of resistance mutations and sex, age, type of immunodepression, mAb treatment and anti-S IgG levels. Model #2 looks for associations between the emergence of resistance mutations and sex, age, SARS-CoV-2 variant, immunodepression and anti-S IgG levels. Positions of dots indicate whether the odds ratios are lower or greater than 1 for each of the variables studied. Whiskers indicate the 95% confidence interval.