Fishilevich, S. | Search bioRxiv

bioRxiv2026

Immune context unmasks regulatory effects of Neanderthal and Denisovan introgression

Neanderthal and Denisovan introgression have left a pervasive footprint in the human genome, yet its regulatory consequences remain poorly understood. Here we use a massively parallel reporter assay to characterize the cis-regulatory activity of 4,161 high-frequency introgressed variants across respiratory (A549), hepatic (HepG2), and hematopoietic (K562) cells exposed to immune and infectious stimuli. We find that ~18% of variants show differential activity between archaic and modern alleles, including 94 whose effects are revealed or modulated by stimulation, often in a cell type-specific manner. We identify loci, including STAT2, IL23A, and RNF41, where clusters of introgressed alleles exert coordinated regulatory effects consistent with adaptive programs. Finally, we dissect the mechanisms underlying the association between Neanderthal introgression and COVID-19 severity and show that the risk allele rs17713054-A, which displays the strongest effect in our assay, increases activity of a TNF--responsive enhancer in lung epithelial cells, directly upregulating SLC6A20.Together, these findings reveal widespread context-dependent regulatory effects of archaic introgression, with broad evolutionary and biomedical implications.

evolutionary biology↗

bioRxiv2026

An information content principle explains regulatory patterns of gene expression across human tissues

Gene expression patterns range from broadly expressed housekeeping genes to highly tissue-specific ones. Notably, many genes exhibit intermediate specificity, characterized by elevated expression in some tissues while low or absent in others. Understanding how regulatory demands scale with tissue specificity offers a valuable opportunity to uncover fundamental principles of genome regulation. By analyzing cis-regulatory element (CRE) counts across human genes with varying tissue specificity, we observed a nonlinear pattern: genes with intermediate specificity harbor the highest CRE count, suggesting distinct regulatory strategies across the expression spectrum. Motivated by this observation, we used the Minimum Description Length (MDL) principle from information theory, together with a maximum parsimony approach from phylogenetics, to quantify regulatory demands across tissues. Our analysis revealed that MDL-based regulatory demand scales consistently with diverse regulatory features, including CRE count, transcription-factor and microRNA targeting, and gene structure. To test whether this scaling changes across the expression spectrum, we partitioned genes by expression breadth. Two patterns emerged: features scaling with MDL in selectively expressed genes tend to act as on/off switches, whereas those in ubiquitous genes serve as fine-tuning knobs. Evolutionary analysis revealed that these regulatory patterns vary with gene age, with alignment between MDL and CRE counts peaking in intermediate-aged genes. Collectively, these results establish MDL combined with maximum parsimony as a powerful framework linking regulatory architecture, expression specificity, and evolutionary age, offering novel insights into the organizational principles underlying genome regulation.

genomics↗