Institute of Molecular Biology

The aim of this study was to identify biological processes associated with neuroaxonal degeneration in multiple sclerosis (MS) by analyzing protein patterns linked to neurofilament light (NfL) protein in CSF and serum samples. The highly sensitive proximity extension assay technology was used to quantify protein levels in paired CSF and serum samples from treatment-naïve, early-stage active MS patients (n = 114). The cohort was stratified into above and below median of CSF-NfL level. A discovery phase identified proteins upregulated in CSF, and a validation cohort (n = 128) confirmed findings in a more clinically diverse MS population, including patients receiving disease-modifying therapies. In the discovery cohort, 13 proteins were upregulated in CSF (leukocyte immunoglobulin like receptor B4, CXCL6, CXCL5, cluster of differentiation 38, CCL19, CXCL11, IMPA1, macrophage scavenger receptor 1 (MSR1), CXCL10, monocyte chemotactic protein 2, ITM2A, TNFRSF12A, and CCL23). It is important to note that associations with CCL19, CXCL11, MSR1, CXCL10, TNFRSF12A, and CCL23 were replicated in a validation MS cohort. By contrast, no consistent serum protein profiles associated with serum levels of NfL could be discerned in this group-based analysis. Furthermore, 75 CSF proteins showed a positive adjusted linear association with CSF-NfL, whereas no such significant association remained in the corresponding analysis in serum. The identified and replicated CSF protein profile indicates distinct inflammatory processes associated with ongoing neuroaxonal degeneration. All of these most robust 6 proinflammatory markers-CCL19, CXCL11, MSR1, CXCL10, TNFRSF12A, and CCL23-independently replicated and were individually observed to be relevant in the context of MS. These findings may further elucidate the immunologic pathways underlying disease processes leading to neuroaxonal degeneration.

Abstract Protein function is governed by molecular interactions, yet structural coverage of these interactions remains sparse. The AlphaFold Protein Structure Database (AFDB) transformed access to accurate monomeric protein structures at scale. Here, we expand AFDB with 1.8M high-confidence protein complexes by conducting a large-scale study of over 31M predicted homo- and heteromeric protein complexes compiled from 4,777 proteomes, including model- and global health organisms, and using STRING physical-interaction annotations. We calibrate confidence metrics to assess the quality of complex predictions, and propose confidence cutoffs. These enabled the discovery of emergent structure and topologies in complex structure prediction that is not present with monomeric predictions. Clustering of high-confidence complexes showed that the largest 1% of non-singleton representatives account for ∼25% of all complexes, and that ∼9% of clusters are conserved across superkingdoms. In summary, large-scale structural predictions of the interactome serve as a foundational resource to facilitate functional and mechanistic discovery across biology.

Abstract Several neurodevelopmental disorders (NDDs) are characterized by impairments in social behavior and affective dysregulation. Converging evidence implicates the endocannabinoid system in the control of both behaviors. However, the brain region-specific contribution of cannabinoid receptor type 1 (CB1R) signaling to these NDD-relevant phenotypes remains unclear. The anterior insular cortex (aINS) is a key integrative hub involved in socio-emotional processing and social novelty recognition. Whether CB1Rs within this region are sufficient to regulate behavioral domains disrupted in NDDs remains unclear. Here, we employed a Cre-dependent viral strategy to selectively restore CB1R mRNA expression in the aINS of global CB1R-deficient mice. Region-specific rescue of CB1R in the aINS normalized social novelty discrimination and reduced anxiety-like behavior as compared to mice lacking CB1R, while leaving basal sociability and locomotor activity unaffected. In addition, insular CB1R re-expression modulated repetitive-like behaviors without broadly altering other behavioral domains. These effects were observed in the absence of off-target expression, supporting the specificity of the genetic manipulation. Our findings demonstrate that CB1R mRNA expression within the aINS is sufficient to regulate distinct socio-emotional and repetitive behavioral domains. These results identify the aINS as a critical CB1-dependent modulatory node and provide mechanistic insight into how region-specific endocannabinoid signaling contributes to behavioral phenotypes relevant to NDDs.

During S phase, the genome is replicated in a tightly regulated spatiotemporal order described as DNA replication timing (RT). Discontinuous lagging-strand synthesis produces Okazaki fragments whose strand-specific distribution reflects replication dynamics. Here, we present RepliCNN, a deep learning framework based on one-dimensional convolutional neural networks to predict RT from Okazaki fragment distributions obtained from strand-specific 3′ DNA end sequencing methods such as GLOE-Seq, TrAEL-seq, or OK-Seq. RepliCNN also automatically annotates replication origins, termination zones, replication fork directionality, and origin efficiency genome-wide from a single dataset. Benchmarking on public and in-house human and yeast datasets using leave-one-chromosome-out cross-validation demonstrates high predictive accuracy in both wild-type and perturbation experiments, enabling comprehensive analyses of replication dynamics from strand-specific DNA 3′ end sequencing data.

Biological fitness quantifies the efficiency and selective advantage of pathogens and hosts in their bilateral interaction. Key questions—such as how much more infectious an emerging variant is compared with its predecessor, or how much protection vaccination offers relative to no vaccination—require fitness to be measured systematically, in real time, and ideally beyond controlled laboratory settings. We propose an approach that infers biological fitness from mostly non-biological data on infection dynamics and contact levels in a population. Because contact levels are expected to predict infection levels under stable biological conditions, systematic deviations between these trends can indicate changes in the underlying processes of transmission or immunity. Using infection surveillance data and GPS co-location information as a proxy for social contact patterns, we apply Bayesian modeling to detect and quantify biological shifts throughout the SARS-CoV-2 pandemic in Germany. We identify substantial regional variation in both the timing and magnitude of fitness changes. These shifts align with the emergence of major variants and the accumulation of population immunity; across Germany, the Alpha, Delta, and Omicron variants were 29 %, 63 %, and 108 % more transmissible than wild-type SARS-CoV-2, respectively. Early natural infection, primary vaccination, and booster vaccination increased population-level immunity by 13 %, 94 %, and 114 % relative to the pre-pandemic naïve state. Our approach provides an immediately deployable framework for detecting biological change during emerging epidemics, given that relevant behavioral and surveillance data are collected in real time. It is particularly valuable for low-resource settings where direct biological measurements may be limited. Significance Statement Biological changes in pathogen transmission remain difficult to measure in real time during acute epidemic and pandemic situations. Here, we showcase a method to extract biological insights from Bayesian modeling of anonymized mobile phone-based GPS-derived contact data and anonymized infection data. We estimate changes in transmissibility associated with virus evolution and vaccination and find agreement with epidemiological, virological and immunological evidence. Increasingly available mobile phone data makes our approach more easily amenable for use in real time. It enables near real-time detection of biological changes in ongoing (e.g. Influenza) and future epidemics and may be especially valuable in low-resource settings.

ABSTRACTGenetic mutations cause approximately 80% of rare human diseases, highlighting the urgent need for precise genome editing. Since clustered regularly interspaced short palindromic repeat (CRISPR)‐CRISPR‐associated 9 (Cas9) nucleases were first used for genome editing in 2012, genome editing technologies have rapidly advanced. Base editors, derived from the CRISPR‐Cas system, were developed to introduce specific point mutations without requiring DNA double‐strand breaks, and subsequently, prime editing (PE) technology was created to enable insertions, deletions, and all types of point mutations. The precision and versatility of PE make it a promising tool for clinical applications. However, PE has potential limitations, including low editing efficiency and limited capacity for large‐scale manipulation. To overcome these limitations, research has been continuously conducted to improve PE efficiency and expand its capabilities. Therefore, this review aims to highlight current efforts and future directions for developing and improving PE‐related tools.

Between 2010 and 2019, international travel increased by approximately 52.2%, highlighting the world's dependence on transportation for global connectivity. Although travel enhances global interactions, it also poses risks to public health through the potential transmission of diseases. The rapid global transmission of infectious diseases, exemplified by the outbreaks of COVID-19 and Zika virus, underscores the critical need for in-depth research into travel-associated disease dissemination. When individuals travel, they are exposed to a variety of diverse microbial environments, which can affect their healthy microbiome. In this review, we introduce the concept of "travel microbiota" to encapsulate the dynamic shifts in human microbial communities induced by travel across different transportation modes. This disruption can affect metabolic and immune functions and potentially facilitate the spread of diseases. Given these implications, it is crucial to investigate how different modes of transportation affect the human microbiota. Our study reviews the impact of travel on the human microbiota, highlighting differences across transportation modes. The objective is to establish a framework for understanding travel health and the role of microbiota in managing travel-related health risks. A comprehensive understanding of this relationship is essential for developing preventive strategies to safeguard and restore the human microbiota. To provide the specific content, relevant publications were identified on Google Scholar, PubMed, and Science Direct using specific keywords such as dysbiosis, gut, health, microbiome, microbiota, pathogens, travel, and transportation. We did not add any limits to the publication date during the inclusion of papers. However, it is noteworthy that the initial reports, including the aforementioned keywords, have been published starting from 2015. Travel has a profound impact on the human microbiota, and it is essential to consider the implications associated with various modes of transportation. Traveling through various modes of transportation, such as roadways, airways, and maritime, has significantly influenced human microbiota. Moreover, it acts as a dynamic interface for microbial exchange driving rapid shift in microbial diversity, community convergence, and the diversification of resistant genes. However, the underlying mechanism of these changes remains elusive. By integrating evidence across multiple modes of transportation, this review highlights travel as an underrecognized determinant of microbiome variability and introduces the term "Travel microbiota". Moreover, this review is pivotal for understanding the ways in which travel alters microbial diversity and developing effective interventions. It is imperative to conduct future research that focuses on conducting large-scale longitudinal studies to assess the effects of traveling on microbial composition and to develop potential preventive measures.

BackgroundImpaired integrin activation on neutrophils is the hallmark of leukocyte adhesion deficiency syndrome in humans, characterized by reduced leukocyte recruitment. The regulation of intracellular calcium levels in neutrophils is important for cellular processes; however, the exact role of store-operated calcium entry (SOCE) and the involvement of stromal interaction molecule (STIM) calcium sensors, and ORAI1-3 calcium channels in neutrophil activation and recruitment is unknown.MethodsUsing an acute kidney injury (AKI) model, intravital microscopy, and biochemical studies, we examined the molecular mechanisms of Ca2+-regulated neutrophil activation and recruitment.ResultsWe demonstrate that STIM1 and ORAI1 in neutrophils are selectively required for E-selectin- and CXCL-1-, but not P-selectin-mediated activation of the β2-integrin CD11a and neutrophil recruitment. Surprisingly, we did not detect an impact of STIM and ORAI isoform expression on neutrophil CD11b activation. Using a clinically relevant murine AKI model, we point out that STIM1- and ORAI1-deficiency in neutrophils prevents neutrophil recruitment into the kidney during sterile inflammation.ConclusionThus, we uncover stimulus specific integrin regulation in neutrophils as a critical determinant of an adequate immune response and pinpoint the clinical relevance of STIM1 and ORAI1.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12964-026-02666-w.