Biology subjects

Seefelder, M.

Publications and source records attributed to Seefelder, M..

4 recordsLinked to original sources

bioRxiv2026

A deep-learning-informed prior and Bayesian model for differential AP-MS interactome analysis

Affinity-purification mass spectrometry (AP-MS) maps a bait proteins partners, but every purification also captures abundant non-specific background that masks genuine interactors. Established tools such as SAINTexpress and CompPASS score one evidence type, treat correlated signals as independent, and ignore prior knowledge of likely interactions. BayesInteractomics, an open-source Julia framework, addresses both limitations by combining machine learning with Bayesian statistics. A neural network trained on protein structures predicts direct binding. A calibrated meta-learner turns this into an informed prior. The prior guides a Bayesian copula-mixture model integrating three AP-MS evidence streams: enrichment, co-abundance, and detection reproducibility. Each candidate receives an interaction probability at a controlled false-discovery rate, optionally updated by structural docking. On synthetic data it ranks first in every benchmark (median AUROC 0.747), and across independent studies it raises high-confidence-call reproducibility from 21% to 79%. It also identifies which interactions are gained or lost between two conditions, unlike established tools.

bioinformatics↗

bioRxiv2026

A meta-analysis resolves the huntingtin interactome into coactivator losses and a robust proteostatic and synaptic gain network

Transcriptional dysregulation and proteostatic collapse are cardinal yet mechanistically separate features of Huntington disease (HD), and how the polyglutamine (polyQ) expansion in huntingtin (HTT) rewires its interactome to produce both remains unresolved. We integrated four published HTT affinity-proteomics datasets and contrasted wild-type and polyQ-expanded HTT within one Bayesian model (BO_SCPLOWAYESC_SCPLOWIO_SCPLOWNTERACTOMICSC_SCPLOW). Of 4,338 proteins, 275 were condition-dependent: the expansion strips HTT of the transcription-activation machinery (Mediator, the ASCOM H3K4-methyltransferase, CREBBP, CDK9) while gaining contacts with the 26S proteasome, HSP70 chaperones and a synaptic and actin-cytoskeletal network, around an intact chaperonin-HAP40 core. This picture emerges only from integration: the datasets overlap so little that a "reproducible in [≥] 2 studies" consensus would recover just [~] 21% of the high-confidence interactors. By reconciling HDs transcriptional and proteotoxic arms within one quantitative interactome, this loss-plus-gain model recasts two historically separate disease mechanisms as complementary and nominates prioritised interfaces (HTT-Mediator/ASCOM, HTT-proteasome) for validation and therapeutic targeting.

neuroscience↗

bioRxiv2026

The huntingtin-HAP40 complex is a bidirectional cellular rheostat

Huntingtin-associated protein 40 (HAP40) is an obligate structural subunit of huntingtin (HTT) and is rapidly degraded when unbound, yet has been conserved across eukaryotes for over a billion years. Combining interactomics, quantitative respirometry, and transcriptomics, we show that the HTT-HAP40 complex functions as a bidirectional stoichiometric rheostat: unbuffered apo-HAP40 activates the Integrated Stress Response via ATF4 and DDIT3/CHOP, whereas unbuffered apo-HTT reciprocally drives cholesterol and fatty-acid biosynthesis through SREBF1/2. We identify the ER-mitochondria tether RMDN3 (PTPIP51) as a key HAP40 interactor, placing mitochondria-associated ER membranes (MAMs) at the rheostats convergence point, and demonstrate that HAP40 depletion specifically impairs respiratory complexes II/IV. Loss of rheostat balance reproduces transcriptional signatures of Huntingtons disease patient tissues, supporting a "dual failure" model in which collapse of stoichiometric buffering -- rather than aggregation toxicity alone -- drives pathogenesis. To our knowledge, this is the first obligate complex in which both unbound partners carry out distinct essential functions, defining stoichiometric buffering as a generalizable regulatory principle that couples complex assembly to metabolic and stress-response control across eukaryotes.

molecular biology↗

bioRxiv2021

A meta-analysis of transcriptomic profiles from Huntington's disease patients substantiates the pathophysiological role of CDC42, NFY, DLX1 and PRMT3

Description of robust transcriptomic alterations in Huntingtons disease is essential to identify targets for biochemical studies and drug development. We analysed publicly available transcriptome data from the brain and blood of 220 HD patients and 241 healthy controls and identified 737 and 661 genes with robustly altered mRNA levels in the brain and blood of HD patients, respectively. In the brain, a subnetwork of 320 genes strongly correlated with HD and was enriched in transport-related genes. Bioinformatical analysis of this subnetwork highlighted CDC42, PAK1, YWHAH, NFY, DLX1, HMGN3, and PRMT3. Moreover, we found that CREB1 can regulate 78.0 % of genes whose mRNA levels correlated with HD in the blood of patients. Alterations in protein transport, metabolism, transcriptional regulation, and CDC42-mediated functions are likely central features of HD. Further our data substantiate the role of transcriptional regulators that have not been reported in the context of HD (e.g. DLX1, HMGN3 and PRMT3) and strongly suggest dysregulation of NFY and its target genes across tissues. A large proportion of the identified genes such as CDC42 were also altered in Parkinsons (PD) and Alzheimers disease (AD). The observed dysregulation of CDC42 and YWHAH in samples from HD, AD and PD patients indicates that those genes and their upstream regulators may be interesting therapeutic targets.

neuroscience↗