Inflammatory bowel disease (IBD) is a complex genetic disease that is instigated and amplified by the confluence of multiple genetic and environmental variables that perturb the immune-microbiome axis. The challenge of dissecting pathological mechanisms underlying IBD has led to the development of transformative approaches in human genetics and functional genomics. Here we describe IBD as a model disease in the context of leveraging human genetics to dissect interactions in cellular and molecular pathways that regulate homeostasis of the mucosal immune system. Finally, we synthesize emerging insights from multiple experimental approaches into pathway paradigms and discuss future prospects for disease-subtype classification and therapeutic intervention.

Pathway paradigms revealed from the genetics of inflammatory bowel disease.

A20 and Cell Death-driven Inflammation

Studies in a variety of species have shown evidence for positively selected variants introduced into a 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.

Detecting adaptive introgression in human evolution using convolutional neural networks.

Ubiquitylation is an essential post-translational modification that regulates intracellular signalling networks by triggering proteasomal substrate degradation, changing the activity of substrates or mediating changes in proteins that interact with substrates. Hundreds of enzymes participate in reversible ubiquitylation of proteins, some acting globally and others targeting specific proteins. Ubiquitylation is essential for innate immune responses, as it facilitates rapid regulation of inflammatory pathways, thereby ensuring sufficient but not excessive responses. A growing number of inborn errors of immunity are attributed to dysregulated ubiquitylation. These genetic disorders exhibit broad clinical manifestations, ranging from susceptibility to infection to autoinflammatory and/or autoimmune features, lymphoproliferation and propensity to malignancy. Many autoinflammatory disorders result from disruption of components of the ubiquitylation machinery and lead to overactivation of innate immune cells. An understanding of the disorders of ubiquitylation in autoinflammatory diseases could enable the development of novel management strategies. 

/pdf/disorders-of-ubiquitylation-unchained-inflammation-3kaw5e53.pdf

Disorders of ubiquitylation: unchained inflammation

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.

https://biorxiv.org/content/10.1101/2020.09.18.301069v1.full.pdf

Detecting adaptive introgression in human evolution using convolutional neural networks

Resisting and tolerating microbes are alternative strategies to survive infection, but little is known about the evolutionary mechanisms controlling this balance. Here genomic analyses of anatomically modern humans, extinct Denisovan hominins and mice revealed a TNFAIP3 allelic series with alterations in the encoded immune response inhibitor A20. Each TNFAIP3 allele encoded substitutions at non-catalytic residues of the ubiquitin protease OTU domain that diminished IκB kinase-dependent phosphorylation and activation of A20. Two TNFAIP3 alleles encoding A20 proteins with partial phosphorylation deficits seemed to be beneficial by increasing immunity without causing spontaneous inflammatory disease: A20 T108A;I207L, originating in Denisovans and introgressed in modern humans throughout Oceania, and A20 I325N, from an N-ethyl-N-nitrosourea (ENU)-mutagenized mouse strain. By contrast, a rare human TNFAIP3 allele encoding an A20 protein with 95% loss of phosphorylation, C243Y, caused spontaneous inflammatory disease in humans and mice. Analysis of the partial-phosphorylation A20 I325N allele in mice revealed diminished tolerance of bacterial lipopolysaccharide and poxvirus inoculation as tradeoffs for enhanced immunity.

Denisovan, modern human and mouse TNFAIP3 alleles tune A20 phosphorylation and immunity.

Unlike classical voltage-gated sodium (NaV) channels, NaX has been characterized as a voltage-insensitive, tetrodotoxin-resistant, sodium (Na+)-activated channel involved in regulating Na+ homeostasis. However, NaX remains refractory to functional characterization in traditional heterologous systems. Here, to gain insight into its atypical physiology, we determine structures of the human NaX channel in complex with the auxiliary β3-subunit. NaX reveals structural alterations within the selectivity filter, voltage sensor-like domains, and pore module. We do not identify an extracellular Na+-sensor or any evidence for a Na+-based activation mechanism in NaX. Instead, the S6-gate remains closed, membrane lipids fill the central cavity, and the domain III-IV linker restricts S6-dilation. We use protein engineering to identify three pore-wetting mutations targeting the hydrophobic S6-gate that unlock a robust voltage-insensitive leak conductance. This constitutively active NaX-QTT channel construct is non-selective among monovalent cations, inhibited by extracellular calcium, and sensitive to classical NaV channel blockers, including tetrodotoxin. Our findings highlight a functional diversity across the NaV channel scaffold, reshape our understanding of NaX physiology, and provide a template to demystify recalcitrant ion channels. 

/pdf/structure-guided-unlocking-of-nax-reveals-a-non-selective-247kbwve.pdf

Structure-guided unlocking of NaX reveals a non-selective tetrodotoxin-sensitive cation channel

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Denisovan, modern human and mouse TNFAIP3 alleles tune A20 phosphorylation and immunity.

Structure-guided unlocking of NaX reveals a non-selective tetrodotoxin-sensitive cation channel