The chromosomes of the human genome are organized in three-dimensions by compartmentalizing the cell nucleus and different genomic loci also interact with each other. However, the principles underlying such 3D genome organization and its functional impact remain poorly understood. In this talk, I will introduce some of our recent work in developing representation learning methods to study single-cell 3D genome organization. Our methods reveal the single-cell chromatin interactome patterns in different cellular conditions and at different scales. We hope that these algorithms will provide new insights into the structure and function of nuclear organization in health and disease.

Complex traits are established through the joint influences of multiple genetic and environmental perturbations. There is a shortage of generalizable principles explaining how molecular networks integrate genetic and environmental effects ultimately leading to complex cellular and organismal traits. In particular, it is poorly understood when and how genetic perturbations lead to molecular changes that are confined to small parts of a network versus when they lead to large-scale adaptations of global network states. Here, we present a concept classifying genetic effects as local, regional or global depending on what fraction of a molecular network they affect. We exemplify this notion using transcriptome, proteome and phospho-proteome profiling of genetically heterogeneous populations of yeast strains, which we integrate with an array of cellular traits. Our analysis identified a central gauge of the yeast molecular network that is related to PKA and TOR (PT) signaling. The resulting ‘PT state’ could be summarized in a single value that explained large parts of the molecular configuration of the strains. This PT state associated with a specific balance between cellular processes spanning energy- and amino acid metabolism, transcription, translation, cell cycle control and cellular stress response. Carbon source quality, oxidative stress, and gene-environment interactions caused monotonic shifts of the molecular network state along the same axis. We further show that complex traits like heat stress resistance and longevity (stationary phase viability) result from the synthesis of genetic effects modulating this PT state with global network effects, plus much more trait-specific effects modulating only small parts of the network. Our work provides a rational for the conditions under which genetic effects propagate through molecular networks with pleiotropic consequences.

Approaches for the identification of disease causal mutations are widely applied in research and clinical settings, but interpretation and ranking of the resulting variants remains challenging. Combined Annotation Dependent Depletion (CADD, https://cadd-sv.bihealth.org/) integrates annotations by contrasting variants that survived purifying selection along the human lineage with simulated mutations to score short sequence variants (SNVs, InDels, multi-allelic substitutions). Since its publication (Kircher, Witten et al. Nat Genet. 2014), CADD was well adopted by the community and minor adjustments and fixes were released since, including the native support of both GRCh37 and GRCh38 assemblies (Rentzsch et al. NAR 2019). Recently, we assessed existing deep neural network (DNN) models for splice effects with the Multiplexed Functional Assay of Splicing using Sort-seq dataset (MFASS, Cheung et al. Mol Cell. 2019). We selected two DNN models based only on genomic sequence, MMSplice and SpliceAI, which showed the best performance for integration into CADD (Rentzsch et al. Genome Med. 2021). The DNN scores boosted CADD's predictions for splice effects and we noted that while the DNN scores have superior performance on splice variants, they fail to account for nonsense and missense effects of the same variants. This suggests that variant prioritization will improve with more domain-specific information and underlines the importance of identifying additional such features, e.g. for regulatory sequences. With rapid advances in the identification of structural variants (SVs), we decided to apply the general concept of CADD to score them (CADD-SV, https://cadd-sv.bihealth.org/). While methods utilizing individual mechanistic principles like the deletion of coding sequence or 3D architecture disruptions were available, a comprehensive tool that uses the broad spectrum of available SV annotations was missing. We show that CADD-SV scores are predictive of pathogenicity and population frequency and that CADD-SV's ability to prioritize pathogenic variants exceeds that of existing methods like SVScore and AnnotSV (Kleinert & Kircher, Genome Res. 2022). Our results highlight advantages of the CADD approach, like profiting from a large training data set covering diverse and rare feature annotations without major ascertainment effects from historic and on-going variant collections.