Biology subjects

Rupp, K.

Publications and source records attributed to Rupp, K..

6 recordsLinked to original sources

bioRxiv2025

Bayesian inference of fitness landscapes via tree-structured branching processes

MotivationThe complex dynamics of cancer evolution, driven by mutation and selection, underlies the molecular heterogeneity observed in tumors. The evolutionary histories of tumors of different patients can be encoded as mutation trees and reconstructed in high resolution from single-cell sequencing data, offering crucial insights for studying fitness effects of and epistasis among mutations. Existing models, however, either fail to separate mutation and selection or neglect the evolutionary histories encoded by the tumor phylogenetic trees. ResultsWe introduce FiTree, a tree-structured multi-type branching process model with epistatic fitness parameterization and a Bayesian inference scheme to learn fitness landscapes from single-cell tumor mutation trees. Through simulations, we demonstrate that FiTree outperforms state-of-the-art methods in inferring the fitness landscape underlying tumor evolution. Applying FiTree to a single-cell acute myeloid leukemia dataset, we identify epistatic fitness effects consistent with known biological findings and quantify uncertainty in predicting future mutational events. The new model unifies probabilistic graphical models of cancer progression with population genetics, offering a principled framework for understanding tumor evolution and informing therapeutic strategies. Availability and implementationThe Python package FiTree and the analysis workflows are available at https://github.com/cbg-ethz/FiTree.

bioinformatics↗

bioRxiv2024

Modeling metastatic progression from cross-sectional cancer genomics data

Metastasis formation is a hallmark of cancer lethality. Yet, metastases are generally unobservable during their early stages of dissemination and spread to distant organs. Genomic datasets of matched primary tumors and metastases may offer insights into the underpinnings and the dynamics of metastasis formation. We present metMHN, a cancer progression model designed to deduce the joint progression of primary tumors and metastases using cross-sectional cancer genomics data. The model elucidates the statistical dependencies among genomic events, the formation of metastasis, and the clinical emergence of both primary tumors and their metastatic counterparts. metMHN enables the chronological reconstruction of mutational sequences and facilitates estimation of the timing of metastatic seeding. In a study of nearly 5000 lung adenocarcinomas, metMHN pinpointed TP53 and EGFR as mediators of metastasis formation. Furthermore, the study revealed that post-seeding adaptation is predominantly influenced by frequent copy number alterations. All datasets and code are available on GitHub at https://github.com/cbg-ethz/metMHN.

bioinformatics↗

bioRxiv2023

Overcoming Observation Bias for Cancer Progression Modeling

Cancers evolve by accumulating genetic alterations, such as mutations and copy number changes. The chronological order of these events is important for understanding the disease, but not directly observable from cross-sectional genomic data. Cancer progression models (CPMs), such as Mutual Hazard Networks (MHNs), reconstruct the progression dynamics of tumors by learning a network of causal interactions between genetic events from their co-occurrence patterns. However, current CPMs fail to include effects of genetic events on the observation of the tumor itself and assume that observation occurs independently of all genetic events. Since a dataset contains by definition only tumors at their moment of observation, neglecting any causal effects on this event leads to the "conditioning on a collider" bias: Events that make the tumor more likely to be observed appear anti-correlated, which results in spurious suppressive effects or masks promoting effects among genetic events. Here, we extend MHNs by modeling effects from genetic progression events on the observation event, thereby correcting for the collider bias. We derive an efficient tensor formula for the likelihood function and learn two models on somatic mutation datasets from the MSK-IMPACT study. In colon adenocarcinoma, we find a strong effect on observation by mutations in TP53, and in lung adenocarcinoma by mutations in EGFR. Compared to classical MHNs, this explains away many spurious suppressive interactions and uncovers several promoting effects. The data, code, and results are available at https://github.com/cbg-ethz/ObservationMHN.

bioinformatics↗

bioRxiv2023

Snow flies self-amputate freezing limbs to sustain behavior at sub-zero temperatures

All living things are profoundly affected by temperature. In spite of the thermodynamic constraints on biology, some animals have evolved to live and move in extremely cold environments. Here, we investigate behavioral mechanisms of cold tolerance in the snow fly (Chionea spp.), a flightless crane fly that is active throughout the winter in boreal and alpine environments of the northern hemisphere. Using thermal imaging, we show that adult snow flies maintain the ability to walk down to an average body temperature of -7 {degrees}C. At this supercooling limit, ice crystallization occurs within the snow flys hemolymph and rapidly spreads throughout the body, resulting in death. However, we discovered that snow flies frequently survive freezing by rapidly amputating legs before ice crystallization can spread to their vital organs. Self-amputation of freezing limbs is a last-ditch tactic to prolong survival in frigid conditions that few animals can endure. Understanding the extreme physiology and behavior of snow insects is urgently important, as the alpine ecosystems they inhabit are being disproportionately altered by anthropogenic climate change.

animal behavior and cognition↗

bioRxiv2021

Categorical encoding of voice in human superior temporal cortex

The ability to recognize abstract features of voice during auditory perception is a complex, yet poorly understood, feat of human audition. For the listener, this occurs in near-automatic fasion to seamlessly extract complex cues from a highly variable auditory signal. Voice perception depends on specialized regions of auditory cortex, including superior temporal gyrus (STG) and superior temporal sulcus (STS). However, the nature of voice encoding at the cortical level remains poorly understoood. We leverage intracerebral recordings across human auditory cortex during presentation of voice and non-voice acoustic stimuli to examine voice encoding in auditory cortex, in eight patient-participants undergoing epilepsy surgery evaluation. We show that voice-selectivity increases along the auditory hierarchy from supratemporal plane (STP) to the STG and STS. Results show accurate decoding of vocalizations from human auditory cortical activity even in the complete absence of linguistic content. These findings show an early, less-selective temporal window of neural activity in the STG and STS followed by a sustained, strongly voice-selective window. We then developed encoding models that demonstrate divergence in the encoding of acoustic features along the auditory hierarchy, wherein STG/STS responses were best explained by voice category as opposed to the acoustic features of voice stimuli. This is in contrast to neural activity recorded from STP, in which responses were accounted for by acoustic features. These findings support a model of voice perception that engages categorical encoding mechanisms within STG and STS. Significance StatementVoice perception occurs via specialized networks in higher order auditory cortex, yet how voice features are encoded remains a central unanswered question. With human intracerebral recordings of auditory cortex, we provide evidence for categorical encoding of voice in STG and STS and occurs in the absence of linguistic content. This selectivity strengthens after an initial onset response and cannot be explained by simple acoustic features. Together, these data support the existence of sites within STG and STS that are specialized for voice perception.

neuroscience↗

bioRxiv2021

Deconstructing the Cortical Sources of Frequency Following Responses to Speech: A Cross-species Approach

Time-varying pitch is a vital cue for human speech perception. Neural processing of time-varying pitch has been extensively assayed using scalp-recorded frequency-following responses (FFRs), an electrophysiological signal thought to reflect integrated phase-locked neural ensemble activity from subcortical auditory areas. Emerging evidence increasingly points to a putative contribution of auditory cortical ensembles to the scalp-recorded FFRs. However, the properties of cortical FFRs and precise characterization of laminar sources are still unclear. Here we used direct human intracortical recordings as well as extra- and intracranial recordings from macaques and guinea pigs to characterize the properties of cortical sources of FFRs to time-varying pitch patterns. We found robust FFRs in the auditory cortex across all species. We leveraged representational similarity analysis as a translational bridge to characterize similarities between the human and animal models. Laminar recordings in animal models showed FFRs emerging primarily from the thalamorecepient layers of the auditory cortex. FFRs arising from these cortical sources significantly contributed to the scalp-recorded FFRs via volume conduction. Our research paves the way for a wide array of studies to investigate the role of cortical FFRs in auditory perception and plasticity. Significance StatementFrequency following responses (FFRs) to speech are scalp-recorded neural signals that inform the fidelity of sound encoding in the auditory system. FFRs, long believed to arise from brainstem and midbrain, have shaped our understanding of sub-cortical auditory processing and plasticity. Non-invasive studies have shown cortical contributions to the FFRs, however, this is still actively debated. Here we employed direct cortical recordings to trace the cortical contribution to the FFRs and characterize the properties of these cortical FFRs. With extra-cranial and intra-cranial recordings within the same subjects we show that cortical FFRs indeed contribute to the scalp-recorded FFRs, and their response properties differ from the sub-cortical FFRs. The findings provide strong evidence to revisit and reframe the FFR driven theories and models of sub-cortical auditory processing and plasticity with careful characterization of cortical and sub-cortical components in the scalp-recorded FFRs.

neuroscience↗