Biology subjects

Fumagalli, M.

Publications and source records attributed to Fumagalli, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Safety profiling of CAR-T cells using an organotypic human tissue platform

CAR-T-cell-associated on-target off-tumor (OTOT) toxicity represents a major safety concern, as recognition of target antigens on healthy tissues can trigger severe and potentially life-threatening complications. Predicting OTOT toxicity remains a challenge because current preclinical models fail to capture the complexity of native human tissues. Here, we developed a human organotypic tissue platform that enables functional assessment of CAR-T-cell activity in intact human tissues across organ-specific and inflammatory contexts. Using a panel of clinically relevant CAR-T-cell products with known OTOT toxicities, we demonstrate that the platform faithfully recapitulates clinically observed tissue-specific toxicity profiles. CAR-T cells targeting EGFR, HER2, and mesothelin induced inflammatory and cytotoxic responses in healthy human lung tissue, whereas CD19 CAR-T cells remained inactive. We further show that OTOT toxicity cannot be reliably predicted from antigen abundance alone but instead results from the integration of multiple target-dependent determinants, including CAR affinity, inflammatory context, antigen accessibility, and effector-cell dose. The platform also enables quantitative assessment of inflammatory and cytotoxic responses and supports evaluation of pharmacological and CAR design-based strategies to mitigate toxicity. Together, this work establishes the first human organotypic platform for functional modeling of CAR-T-cell-associated OTOT toxicity, providing a clinically relevant framework for preclinical safety evaluation and the rational development of safer engineered cell therapies. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=167 SRC="FIGDIR/small/740527v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@12c8addorg.highwire.dtl.DTLVardef@150d392org.highwire.dtl.DTLVardef@17218f7org.highwire.dtl.DTLVardef@1c54078_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2026

Selective targeting of the oligodendroglial GPR17 receptor improves myelin integrity and motor function in female SOD1G93A mice

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with no definitive disease-modifying therapies available, underscoring the urgent need to identify novel druggable targets. The G protein-coupled receptor GPR17 is a critical regulator of oligodendrocyte maturation and has emerged as a candidate target in ALS, yet its relevance to human disease and its therapeutic potential remain unclear. Here, we demonstrate that pathological GPR17 upregulation defines a conserved, pathologically immature oligodendroglial state in human ALS that can be pharmacologically leveraged to restore myelin integrity and improve functional outcome in vivo. Publicly available transcriptomics datasets and histological analyses revealed an increased abundance of GPR17-expressing immature oligodendrocytes in post-mortem human spinal cord tissue from ALS cases. Moreover, sustained activation of GPR17 with a selective agonist induced GPR17 internalization in heterologous expression systems and promoted the differentiation of primary oligodendrocyte precursors derived from SOD1G93A mice. Translating these findings in vivo, chronic treatment with a brain-penetrant GPR17 agonist derived from the same pharmacological class significantly extended survival, delayed body weight loss, and improved motor performance in female SOD1G93A mice, whereas male mice showed no therapeutic benefit. These effects were associated with restored oligodendrocyte maturation, preserved myelin integrity, motor neuron survival, and attenuated reactive gliosis in the spinal cord of female SOD1G93A mice, while milder effects were observed in males. Together, these findings establish oligodendroglial GPR17 as a conserved and pharmacologically actionable target in ALS and show that sustained in vivo GPR17 agonism can reprogram altered oligodendroglial states and slow disease progression in a sex-dependent manner.

pharmacology and toxicology↗

bioRxiv2025

Semi-supervised detection of natural selection with positive-unlabeled learning

Identifying genomic regions shaped by natural selection is a central goal in evolutionary ge-nomics. Existing machine learning methods for this task are typically trained using simulated genomic data labeled according to specific evolutionary scenarios. While effective in controlled settings, these models are limited by their reliance on explicit class labels. They can only detect the specific processes they were trained to recognize, making it difficult to interpret predictions for regions influenced by other evolutionary forces. This limitation is especially problematic when analyzing empirical genomes shaped by a mixture of adaptive, demographic, and ecolog-ical factors. One-vs.-rest strategies offer a potential alternative, but suffer from the inherent complexity of modeling all other evolutionary and demographic processes as a catch-all "rest" class. Here, we explore positive-unlabeled learning as a more flexible framework for detection of adaptive events. Positive-unlabeled learning is a semi-supervised approach that permits iden-tification of samples of a target class using only positive labels and an unlabeled background, without requiring explicit modeling of negative samples. To assess the utility of this approach, we focus on a binary classification setting for detecting selective sweeps (positive samples) aris-ing from positive natural selection against a mixed background composed of both unlabeled sweeps and neutrally-evolving regions. To accomplish this goal, we introduce PULSe, a method that employs only a set of labeled sweep observations for training while treating all remaining data as unlabeled. By avoiding assumptions about the composition of the background, PULSe enables robust sweep discovery in realistic genomic landscapes. We systematically evaluate its performance across a range of demographic, adaptive, and confounding contexts, including domain shift arising from misspecified demographic models, and find that PULSe delivers high performance and generalizability. To demonstrate a practical application of PULSe, we ana-lyzed European and Bengali in Bangladesh human genomes, treating the empirical genomes as unlabeled sets, and recapitulating several previously-identified sweep candidates. Our results show that PULSe provides a powerful and versatile alternative for detecting adaptive regions, with the potential to generalize across a range of genomic landscapes.

genomics↗

bioRxiv2025

On the use of generative models for evolutionary inference of malaria vectors from genomic data

Malaria in sub-Saharan Africa is transmitted by mosquitoes, in particular the Anopheles gambiae complex. Efforts to control the spread of malaria have often focused on these vectors, but less is known about the demographic history of populations and species in the Anopheles complex. Here, we first quantify the genetic structure of mosquito populations in sub-Saharan Africa using unsupervised machine learning. We then adapt and apply an innovative generative deep learning algorithm to infer the joint evolutionary history of populations sampled in Guinea and Burkina Faso, West Africa. We further develop a novel model selection approach and discover that an evolutionary model with migration fits this pair of populations better than a model without post-split migration. For the migration model, we find that our method accurately captures population genetic differentiation. These findings demonstrate that machine learning and generative models are a valuable direction for future understanding of the evolution of malaria vectors, including the joint inference of demography and natural selection. Understanding changes in population size, migration patterns, and adaptation in hosts, vectors, and pathogens will assist malaria control interventions, with the ultimate goal of predicting nuanced outcomes from insecticide resistance to population collapse.

genomics↗

bioRxiv2025

An in vivo CRISPR screen unveils top target genes to efficiently improve CAR-T cell persistence in a solid tumour model of lung adenocarcinoma

CAR-T cell therapies are revolutionizing the treatment of refractory and relapsed haematological malignancies, but many patients do not exhibit long-term responses, and these therapies are less effective against solid tumors. Poor persistence of CAR-T cells in patients is associated with therapeutic failure, highlighting the need to identify strategies promoting in vivo expansion. Here, we developed an in vivo competitive screening method to identify genes whose inactivation confers a selective advantage to CAR-T cells. Inactivation of 50 genes in a heterogeneous population of T cells expressing an EGFR-targeting CAR revealed that disruption of REGNASE-1, SOCS1, PTPN2, and P16/NK4A conferred a selective advantage to CAR-T cells in human lung tumor-bearing mice. Consistently, inactivation of these genes improved tumor eradication by CAR-T cells. Interestingly disruption of other genes, described to improve CAR-T cell function in other contexts, had a negative impact in this orthotopic lung tumor model. Further evaluation of long-term effects in a subcutaneous model, highlighted SOCS1 ablation as the most promising strategy for in vivo CAR-T cell amelioration. These results support the importance of evaluating CAR-T cell editing strategies in tumor-specific models and highlight the versatility of our screening approach as a pre-clinical tool for context-specific studies on CAR-T cells amelioration.

immunology↗

bioRxiv2025

Iron and zinc biofortification in potato through the introduction of NICOTIANAMINE SYNTHASE and FERRITIN genes

Micronutrient deficiencies, such as those involving iron, zinc, and vitamin A, represent a critical global public health challenge. Crop biofortification offers a cost-effective solution by enhancing the micronutrient content of staple foods, particularly in regions with limited access to diverse diets. In this study, we developed a high-iron and high-zinc Spunta potato variety through the co-expression of the Arabidopsis thaliana NICOTIANAMINE SYNTHASE 1 (AtNAS1) gene under a constitutive promoter and the Phaseolus vulgaris FERRITIN (PvFERRITIN) gene under a tuber-specific promoter. Of the five co-expressing events evaluated, we identified FN4 as the optimal line, achieving significant levels of iron and zinc biofortification without compromising yield. This line exhibited an enhanced capacity to accumulate iron in tubers under conditions of increased soil iron availability, maintaining yield even when vegetative growth was reduced due to elevated soil iron levels. The FN4 variety showed a 2.1-and 1.8-fold increase in iron and zinc content, respectively, in the tubers. A typical portion of this variety could provide 33.3% of the recommended dietary allowance (RDA) for iron and 19.8% for zinc in women of reproductive age. This biofortified potato has the potential to reduce iron deficiency anemia and zinc deficiency, enhancing the health of populations in both rural and urban areas affected by poverty.

plant biology↗

bioRxiv2025

Efficient detection and characterization of targets of natural selection using transfer learning

Natural selection leaves detectable patterns of altered spatial diversity within genomes, and identifying affected regions is crucial for understanding species evolution. Recently, machine learning approaches applied to raw population genomic data have been developed to uncover these adaptive signatures. Convolutional neural networks (CNNs) are particularly effective for this task, as they handle large data arrays while maintaining element correlations. However, shallow CNNs may miss complex patterns due to their limited capacity, while deep CNNs can capture these patterns but require extensive data and computational power. Transfer learning addresses these challenges by utilizing a deep CNN pre-trained on a large dataset as a feature extraction tool for downstream classification and evolutionary parameter prediction. This approach reduces extensive training data generation requirements and computational needs while maintaining high performance. In this study, we developed TrIdent, a tool that uses transfer learning to enhance detection of adaptive genomic regions from image representations of multilocus variation. We evaluated TrIdent across various genetic, demographic, and adaptive settings, in addition to unphased data and other confounding factors. TrIdent demonstrated improved detection of adaptive regions compared to recent methods using similar data representations. We further explored model interpretability through class activation maps and adapted TrIdent to infer selection parameters for identified adaptive candidates. Using whole-genome haplotype data from European and African populations, TrIdent effectively recapitulated known sweep candidates and identified novel cancer, and other disease-associated genes as potential sweeps.

evolutionary biology↗

bioRxiv2024

The importance of small island populations for the long term survival of endangered large-bodied insular mammals

Island populations of large vertebrates have experienced higher extinction rates than mainland populations over long timescales due to demographic stochasticity, genetic drift and inbreeding. Conversely, small island populations often experience relatively less anthropogenic habitat degradation than populations on larger islands, making them potential targets for conservation interventions such as genetic rescue to improve their overall genetic diversity. Here we determine the consequences and conservation implications of long-term isolation and recent human activities on genetic diversity of island populations of two forest-dependent mammals endemic to the Wallacea archipelago: the anoa (Bubalus spp.) and babirusa (Babyrousa spp.). Using genomic analyses and habitat suitability models, we show that, compared to closely related species, populations on mainland Sulawesi exhibit low heterozygosity, high inbreeding, and a high proportion of deleterious alleles. In contrast, populations on smaller islands possess fewer deleterious mutations despite exhibiting lower heterozygosity and higher inbreeding. Site frequency spectra indicate that these patterns reflect stronger, long-term purging in smaller island populations. Our results thus suggest that conservation efforts should focus on protecting small island habitats and avoiding translocations from mainland populations. This study highlights the crucial role of small offshore islands for the long-term survival of Wallaceas iconic and indigenous mammals in the face of development on the mainland. Significance statementWithin tropical archipelagos, such as the Wallacea biodiversity hotspot, larger islands experience greater resource exploitation compared to smaller ones, highlighting the potential of smaller islands as refuges for conservation. To investigate the genetic health of populations on small islands, we used genomic, occurrence, and environmental data from a system of replicated populations of anoa and babirusa across islands of varying sizes. In contrast to larger islands like Sulawesi, our results demonstrate that smaller offshore islands not only provide higher-quality habitats but also support populations that have efficiently purged harmful mutations. Thus, despite their known vulnerability over geological time-frames, small island populations can provide long term insurance against human-driven extinction and conservation efforts should prioritise habitat management over translocations.

evolutionary biology↗

bioRxiv2024

Mitochondrial metabolism sustains CD8+ T cellmigration for an efficient infiltration into solid tumors

The ability of CD8+ T cells to infiltrate solid tumors and reach cancer cells is associated with improved patient survival and responses to immunotherapy. Thus, identifying the factors controlling T cell migration in tumors is critical, so that strategies to intervene on these targets can be developed. Although interstitial motility is a highly energy-demanding process, the metabolic requirements of CD8+ T cells migrating in a 3D environment remain unclear. Here, we demonstrate that the tricarboxylic acid (TCA) cycle is the main metabolic pathway sustaining human CD8+ T cell motility in 3D collagen gels and tumor slices while glycolysis plays a much minor role. Using pharmacological and genetic approaches, we report that CD8+ T cell migration depends on the mitochondrial oxidation of glucose and glutamine, but not fatty acids, and both ATP and ROS produced by mitochondria are required for T cells to migrate. Pharmacological interventions to increase mitochondrial activity improve CD8+ T cells intra-tumoral migration and CAR T cell recruitment into tumor islets leading to better control of tumor growth in human xenograft models. Our study highlights the rationale of targeting mitochondrial metabolism to enhance the migration and antitumor efficacy of CAR T cells in treating solid tumors.

immunology↗

bioRxiv2022

The genomic footprint of social stratification in admixing American populations

Cultural and socioeconomic differences stratify human societies and shape their genetic structure beyond the sole effect of geography. Despite mating being limited by the permeability of sociocultural stratification, most demographic models in population genetics often assume random mating. Taking advantage of the correlation between sociocultural stratification and the proportion of genetic ancestry in admixed populations, we sought to infer the former process in the Americas. To this aim, we define a mating model where the individual proportions of the genome inherited from Native American, European and sub-Saharan African ancestral populations constrain the mating probabilities through ancestry-related assortative mating and sex bias parameters. We simulate a wide range of admixture scenarios under this model. Then, we train a deep neural network and retrieve good performance in predicting mating parameters from genomic data. Our results show how population stratification shaped by racial and gender hierarchies have constrained the admixture processes in the Americas since the European colonisation and the subsequent Atlantic slave trade.

genetics↗

bioRxiv2022

Simultaneous Inference of Past Demography and Selection from the Ancestral Recombination Graph under the Beta Coalescent

The reproductive mechanism of a species is a key driver of genome evolution. The standard Wright-Fisher model for the reproduction of individuals in a population assumes that each individual produces a number of offspring negligible compared to the total population size. Yet many species of plants, invertebrates, prokaryotes or fish exhibit neutrally skewed offspring distribution or strong selection events yielding few individuals to produce a number of offspring of up to the same magnitude as the population size. As a result, the genealogy of a sample is characterized by multiple individuals (more than two) coalescing simultaneously to the same common ancestor. The current methods developed to detect such multiple merger events do not account for complex demographic scenarios or recombination, and require large sample sizes. We tackle these limitations by developing two novel and different approaches to infer multiple merger events from sequence data or the ancestral recombination graph (ARG): a sequentially Markovian coalescent (SM{beta}C) and a graph neural network (GNNcoal). We first give proof of the accuracy of our methods to estimate the multiple merger parameter and past demographic history using simulated data under the {beta}-coalescent model. Secondly, we show that our approaches can also recover the effect of positive selective sweeps along the genome. Finally, we are able to distinguish skewed offspring distribution from selection while simultaneously inferring the past variation of population size. Our findings stress the aptitude of neural networks to leverage information from the ARG for inference but also the urgent need for more accurate ARG inference approaches.

genetics↗

bioRxiv2021

Disentangling signatures of selection before and after European colonization in Latin Americans

Throughout human evolutionary history, large-scale migrations have led to intermixing (i.e., admixture) between previously separated human groups. While classical and recent work have shown that studying admixture can yield novel historical insights, the extent to which this process contributed to adaptation remains underexplored. Here, we introduce a novel statistical model, specific to admixed populations, that identifies loci under selection while determining whether the selection likely occurred post-admixture or prior to admixture in one of the ancestral source populations. Through extensive simulations we show that this method is able to detect selection, even in recently formed admixed populations, and to accurately differentiate between selection occurring in the ancestral or admixed population. We apply this method to genome-wide SNP data of ~4,000 individuals in five admixed Latin American cohorts from Brazil, Chile, Colombia, Mexico and Peru. Our approach replicates previous reports of selection in the HLA region that are consistent with selection post-admixture. We also report novel signals of selection in genomic regions spanning 47 genes, reinforcing many of these signals with an alternative, commonly-used local-ancestry-inference approach. These signals include several genes involved in immunity, which may reflect responses to endemic pathogens of the Americas and to the challenge of infectious disease brought by European contact. In addition, some of the strongest signals inferred to be under selection in the Native American ancestral groups of modern Latin Americans overlap with genes implicated in energy metabolism phenotypes, plausibly reflecting adaptations to novel dietary sources available in the Americas.

genomics↗

bioRxiv2021

Neutralizing antibody-independent immunity to SARS-CoV-2 in hamsters and hACE-2 transgenic mice immunized with a RBD/Nucleocapsid fusion protein

The nucleocapsid (N) and the receptor binding domain (RBD) of the Spike (S) proteins elicit robust antibody and T cell responses either in vaccinated or COVID-19 convalescent individuals. We generated a chimeric protein that comprises the sequences of RBD from S and N antigens (SpiN). SpiN was highly immunogenic and elicited a strong IFN{gamma} response from T cells and high levels of antibodies to the inactivated virus, but no neutralizing antibodies. Importantly, hamsters and the human Angiotensin Convertase Enzyme-2-transgenic mice immunized with SpiN were highly resistant to challenge with the wild type SARS-CoV-2, as indicated by viral load, clinical outcome, lung inflammation and lethality. Thus, the N protein should be considered to induce T-cell-based immunity to improve SARS-CoV-2 vaccines, and eventually to circumvent the immune scape by variants.

immunology↗

bioRxiv2021

HMMploidy: inference of ploidy levels from short-read sequencing data

The inference of ploidy levels from genomic data is important to understand molecular mechanisms underpinning genome evolution. However, current methods based on allele frequency and sequencing depth variation do not have power to infer ploidy levels at low-and mid-depth sequencing data, as they do not account for data uncertainty. Here we introduce HMMploidy, a novel tool that leverages the information from multiple samples and combines the information from sequencing depth and genotype likelihoods. We demonstrate that HMMploidy outperforms existing methods in most tested scenarios, especially at low-depth with large sample size. We apply HMMploidy to sequencing data from the pathogenic fungus Cryptococcus neoformans and retrieve pervasive patterns of aneuploidy, even when artificially downsampling the sequencing data. We envisage that HMMploidy will have wide applicability to low-depth sequencing data from polyploid and aneuploid species.

bioinformatics↗

bioRxiv2020

Detecting adaptive introgression in human evolution using convolutional neural networks

Studies in a variety of species have shown evidence for positively selected variants introduced into one population via introgression from another, distantly related population--a process known as adaptive introgression. However, there are few explicit frameworks for jointly modelling introgression and positive selection, in order to detect these variants using genomic sequence data. Here, we develop an approach based on convolutional neural networks (CNNs). CNNs do not require the specification of an analytical model of allele frequency dynamics, and have outperformed alternative methods for classification and parameter estimation tasks in various areas of population genetics. Thus, they are potentially well suited to the identification of adaptive introgression. Using simulations, we trained CNNs on genotype matrices derived from genomes sampled from the donor population, the recipient population and a related non-introgressed population, in order to distinguish regions of the genome evolving under adaptive introgression from those evolving neutrally or experiencing selective sweeps. Our CNN architecture exhibits 95% accuracy on simulated data, even when the genomes are unphased, and accuracy decreases only moderately in the presence of heterosis. As a proof of concept, we applied our trained CNNs to human genomic datasets--both phased and unphased--to detect candidates for adaptive introgression that shaped our evolutionary history.

evolutionary biology↗

bioRxiv2020

Distinguishing between recent balancing selection and incomplete sweep using deep neural networks

1Balancing selection is an important adaptive mechanism underpinning a wide range of phenotypes. Despite its relevance, the detection of recent balancing selection from genomic data is challenging as its signatures are qualitatively similar to those left by ongoing positive selection. In this study we developed and implemented two deep neural networks and tested their performance to predict loci under recent selection, either due to balancing selection or incomplete sweep, from population genomic data. Specifically, we generated forward-in-time simulations to train and test an artificial neural network (ANN) and a convolutional neural network (CNN). ANN received as input multiple summary statistics calculated on the locus of interest, while CNN was applied directly on the matrix of haplotypes. We found that both architectures have high accuracy to identify loci under recent selection. CNN generally outperformed ANN to distinguish between signals of balancing selection and incomplete sweep and was less affected by incorrect training data. We deployed both trained networks on neutral genomic regions in European populations and demonstrated a lower false positive rate for CNN than ANN. We finally deployed CNN within the MEFV gene region and identified several common variants predicted to be under incomplete sweep in a European population. Notably, two of these variants are functional changes and could modulate susceptibility to Familial Mediterranean Fever, possibly as a consequence of past adaptation to pathogens. In conclusion, deep neural networks were able to characterise signals of selection on intermediate-frequency variants, an analysis currently inaccessible by commonly used strategies.

evolutionary biology↗

bioRxiv2018

Chorioamnionitis Is a Risk Factor for Intraventricular Hemorrhage in Preterm Infants: A Systematic Review and Meta-Analysis

Although chorioamnionitis (CA) is a well-known risk factor for white matter disease of prematurity, the association with intraventricular hemorrhage (IVH) is controversial and has not been yet systematically reviewed. We performed a systematic review and meta-analysis of studies exploring the association between CA and IVH. A comprehensive literature search was conducted using PubMed/MEDLINE and EMBASE, from their inception to 1 July 2017. Studies were included if they examined preterm infants and reported primary data that could be used to measure the association between exposure to CA and the presence of IVH. A random-effects model was used to calculate odds ratios (OR) and 95% confidence intervals (CI). We found 1284 potentially relevant studies, of which 85 met the inclusion criteria (46,244 infants, 13,432 CA cases). Meta-analysis showed that CA exposure was significantly associated with all grades IVH (OR 1.88, 95% CI 1.61-2.19), with grades 1-2 IVH (OR 1.69, 95% CI 1.22-2.34), and with grades 3-4 IVH (OR 1.62, 95% CI 1.42-1.85). Both clinical and histological CA were associated with an increased risk for developing IVH in very preterm infants. In contrast, the presence of funisitis did not increase IVH risk when compared to CA in the absence of funisitis (OR 1.22, 95% CI 0.89-1.67). Further meta-analyses confirmed earlier findings that CA-exposed infants have significantly lower gestation age (GA; mean difference [MD] -1.20 weeks) and lower birth weight (BW; MD -55g) than the infants not exposed to CA. However, meta-regression and subgroup analysis could not demonstrate an association between the lower GA and BW and the risk of IVH in the CA-exposed infants. In conclusion, our data show that CA is a risk factor for IVH, but also a risk factor for greater prematurity and more clinical instability. In contrast to other complications of prematurity, such as patent ductus arteriosus, retinopathy of prematurity, or bronchopulmonary dysplasia, the effect of CA on IVH appears to be independent of CA as causative factor for very preterm birth.

developmental biology↗

bioRxiv2018

Evolution of the CLTCL1 Gene Encoding CHC22 Clathrin Reveals Selection Influencing CHC22’s Role in Human Glucose Metabolism

CHC22 clathrin plays a key role in intracellular membrane traffic of the insulin-responsive glucose transporter GLUT4 in humans. We performed population genetic and phylogenetic analyses of the CHC22-encoding CLTCL1 gene, revealing independent gene loss in at least two vertebrate lineages, after arising from gene duplication. All vertebrates retained the paralogous CLTC gene encoding CHC17 clathrin, which mediates endocytosis. For vertebrates retaining CLTCL1, strong evidence for purifying selection supports CHC22 functionality. All human populations maintained two high frequency CLTCL1 allelic variants, encoding either methionine or valine at position 1316. Functional studies indicated that CHC22-V1316, which is more frequent in farming populations than in hunter-gatherers, has different cellular dynamics than M1316-CHC22 and is less effective at controlling GLUT4 membrane traffic, attenuating its insulin-regulated response. These analyses suggest that ancestral human dietary change influenced selection of allotypes that affect CHC22s role in metabolism and have potential to differentially influence the human insulin response.

genetics↗