Background: Disease is a major factor driving the evolution of many organisms. In honey bees, selection for social behavioral responses is the primary adaptive process facilitating disease resistance. One such process, hygienic behavior, enables bees to resist multiple diseases, including the damaging parasitic mite Varroa destructor. The genetic elements and biochemical factors that drive the expression of these adaptations are currently unknown. Proteomics provides a tool to identify proteins that control behavioral processes, and these proteins can be used as biomarkers to aid identification of disease tolerant colonies. Results: We sampled a large cohort of commercial queen lineages, recording overall mite infestation, hygiene, and the specific hygienic response to V. destructor. We performed proteome-wide correlation analyses in larval integument and adult antennae, identifying several proteins highly predictive of behavior and reduced hive infestation. In the larva, response to wounding was identified as a key adaptive process leading to reduced infestation, and chitin biosynthesis and immune responses appear to represent important disease resistant adaptations. The speed of hygienic behavior may be underpinned by changes in the antenna proteome, and chemosensory and neurological processes could also provide specificity for detection of V. destructor in antennae. Conclusions: Our results provide, for the first time, some insight into how complex behavioural adaptations manifest in the proteome of honey bees. The most important biochemical correlations provide clues as to the underlying molecular mechanisms of social and innate immunity of honey bees. Such changes are indicative of potential divergence in processes controlling the hive-worker maturation.

The Western honey bee (Apis mellifera L.) is a critical component of human agriculture through its pollination activities. For years, beekeepers have controlled deadly pathogens such as Paenibacillus larvae, Nosema spp. and Varroa destructor with antibiotics and pesticides but widespread chemical resistance is appearing and most beekeepers would prefer to eliminate or reduce the use of in-hive chemicals. While such treatments are likely to still be needed, an alternate management strategy is to identify and select bees with heritable traits that allow them to resist mites and diseases. Breeding such bees is difficult as the tests involved to identify disease-resistance are complicated, time-consuming, expensive and can misidentify desirable genotypes. Additionally, we do not yet fully understand the mechanisms behind social immunity. Here we have set out to discover the molecular mechanism behind hygienic behavior (HB), a trait known to confer disease resistance in bees. After confirming that HB could be selectively bred for, we correlated measurements of this behavior with protein expression over a period of three years, at two geographically distinct sites, using several hundred bee colonies. By correlating the expression patterns of individual proteins with HB scores, we identified seven putative biomarkers of HB that survived stringent control for multiple hypothesis testing. Intriguingly, these proteins were all involved in semiochemical sensing (odorant binding proteins), nerve signal transmission or signal decay, indicative of the series of events required to respond to an olfactory signal from dead or diseased larvae. We then used recombinant versions of two odorant-binding proteins to identify the classes of ligands that these proteins might be helping bees detect. Our data suggest that neurosensory detection of odors emitted by dead or diseased larvae is the likely mechanism behind a complex and important social immunity behavior that allows bees to co-exist with pathogens.

Human cells consist of a complex hierarchy of components, many of which remain unexplored^1,2. Here we construct a global map of human subcellular architecture through joint measurement of biophysical interactions and immunofluorescence images for over 5,100 proteins in U2OS osteosarcoma cells. Self-supervised multimodal data integration resolves 275 molecular assemblies spanning the range of 10⁻⁸ to 10⁻⁵ m, which we validate systematically using whole-cell size-exclusion chromatography and annotate using large language models³. We explore key applications in structural biology, yielding structures for 111 heterodimeric complexes and an expanded Rag–Ragulator assembly. The map assigns unexpected functions to 975 proteins, including roles for C18orf21 in RNA processing and DPP9 in interferon signalling, and identifies assemblies with multiple localizations or cell type specificity. It decodes paediatric cancer genomes⁴, identifying 21 recurrently mutated assemblies and implicating 102 validated new cancer proteins. The associated Cell Visualization Portal and Mapping Toolkit provide a reference platform for structural and functional cell biology.

Spinal cord injury (SCI) is a devastating neurological condition for which there are currently no effective treatment options to restore function. A major obstacle to the development of new therapies is our fragmentary understanding of the coordinated pathophysiological processes triggered by damage to the human spinal cord. Here, we describe a systems biology approach to integrate decades of small-scale experiments with unbiased, genome-wide gene expression from the human spinal cord, revealing a gene regulatory network signature of the pathophysiological response to SCI. Our integrative analyses converge on an evolutionarily conserved gene subnetwork enriched for genes associated with the response to SCI by small-scale experiments, and whose expression is upregulated in a severity-dependent manner following injury and downregulated in functional recovery. We validate the severity-dependent upregulation of this subnetwork in rodents in primary transcriptomic and proteomic studies. Our analysis provides systems-level view of the coordinated molecular processes activated in response to SCI.

Background: Manipulative parasites are thought to liberate molecules in their external environment, acting as manipulation factors with biological functions implicated in their host’s physiological and behavioural alterations. These manipulation factors are part of a complex mixture called the secretome. While the secretomes of various parasites have been described, there is very little data for a putative manipulative parasite. It is necessary to study the molecular interaction between a manipulative parasite and its host to better understand how such alterations evolve. Methods: Here, we used proteomics to characterize the secretome of a model cestode with a complex life cycle based on trophic transmission. We studied Schistocephalus solidus during the life stage in which behavioural changes take place in its obligatory intermediate fish host, the threespine stickleback (Gasterosteus aculeatus). We produced a novel genome sequence and assembly of S. solidus to improve protein coding gene prediction and annotation for this parasite. We then described the whole worm’s proteome and its secretome during fish host infection using LC–MS/MS. Results: A total of 2290 proteins were detected in the proteome of S. solidus, and 30 additional proteins were detected specifically in the secretome. We found that the secretome contains proteases, proteins with neural and immune functions, as well as proteins involved in cell communication. We detected receptor-type tyrosine-protein phosphatases, which were reported in other parasitic systems to be manipulation factors. We also detected 12 S. solidus-specific proteins in the secretome that may play important roles in host–parasite interactions. Conclusions: Our results suggest that S. solidus liberates molecules with putative host manipulation functions in the host and that many of them are species-specific. Graphical

Purpose: Accumulating analyses of pro-oncogenic molecular mechanisms triggered a rapid development of targeted cancer therapies. Although many of these treatments produce impressive initial responses, eventual resistance onset is practically unavoidable. One of the main approaches for preventing this refractory condition relies on the implementation of combination therapies. This includes dual-specificity reagents that affect both of their targets with a high level of selectivity. Unfortunately, selection of target combinations for these treatments is often confounded by limitations in our understanding of tumor biology. Here, we describe and validate a multipronged unbiased strategy for predicting optimal co-targets for bispecific therapeutics. Experimental Design: Our strategy integrates ex vivo genome-wide loss-of-function screening, BioID interactome profiling, and gene expression analysis of patient data to identify the best fit co-targets. Final validation of selected target combinations is done in tumorsphere cultures and xenograft models. Results: Integration of our experimental approaches unambiguously pointed toward EGFR and EPHA2 tyrosine kinase receptors as molecules of choice for co-targeting in multiple tumor types. Following this lead, we generated a human bispecific anti-EGFR/EPHA2 antibody that, as predicted, very effectively suppresses tumor growth compared with its prototype anti-EGFR therapeutic antibody, cetuximab. Conclusions: Our work not only presents a new bispecific antibody with a high potential for being developed into clinically relevant biologics, but more importantly, successfully validates a novel unbiased strategy for selecting biologically optimal target combinations. This is of a significant translational relevance, as such multifaceted unbiased approaches are likely to augment the development of effective combination therapies for cancer treatment. See related commentary by Kumar, p. 2570