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Biology subjects

Johansson, M.

Publications and source records attributed to Johansson, M..

At least 19 recordsLinked to original sources

CD4 T cell surface architecture is associated with response to anti-PD-1 therapy in non-small cell lung cancer

Anti-PD-1 therapy has substantially improved outcomes for patients with advanced non-small cell lung cancer (NSCLC), but clinical responses remain highly heterogeneous and are difficult to predict. Current immune profiling approaches primarily characterize immune cell composition and protein abundance, whereas the spatial organization of proteins at the cell surface has remained largely unexplored. Here, we demonstrate for the first time that cell surface protein organization is associated with clinical response to immune checkpoint inhibition therapy. Using the Proximity Network Assay (PNA), we simultaneously quantified cell frequencies, protein abundance, and protein organization in pretreatment peripheral blood mononuclear cells from 62 patients with advanced NSCLC treated with anti-PD-1 agents. While most cell populations were unchanged between responders and non-responders, pronounced response-associated alterations were identified within the CD4 T-cell compartment. Responders exhibited elevated expression of adhesion-associated proteins, and remodeling of a CD5-containing membrane domain. Increased colocalization within this domain was associated with poor clinical outcome and reduced survival. Machine learning analysis further identified CD4 T cell features as the strongest predictors of treatment response and demonstrated that protein organization provided greater predictive power than protein abundance alone. Together, these findings identify cell surface protein organization as a previously unexplored source of biomarkers and highlight its potential to improve immune profiling and biomarker discovery in cancer immunotherapy.

immunology↗

Spaghettification: How extreme replichore imbalance impacts bacterial replication and morphology

Bacterial chromosomes are typically organized into two similarly sized replichores to synchronize bidirectional replication and fusion within a Tus/ter-defined termination zone. Previous studies have suggested that replichore balance in both Salmonella and E. coli is maintained by selection for efficient and properly coordinated replication termination and chromosome segregation. However, there is a lack of understanding of the causal relationship between replichore asymmetry, cellular fitness, and replication-fork convergence, as well as how the termination machinery behaves when the two replichores differ substantially in length. Here we dissect the impact of replichore asymmetry by engineering 41 isogenic Salmonella inversion mutants spanning progressive imbalances, complemented by duplication-based asymmetries. Our constructed strains show that even cells with severely imbalanced chromosomes, where one replichore is 11-fold longer in size than the other (0.38 Mb vs. 4.48 Mb) are viable. However, relative fitness declined linearly with replichore size difference at approximately -0.17 per Mb. Additionally, highly imbalanced strains exhibited substantial division defects with severe cell elongation and lysis. Replication profiling showed that, even under extreme replichore asymmetry, forks consistently converge at the innermost ter sites of the shorter replichore and that two sequential Tus/ter barriers were sufficient for complete replication fork arrest. Together, these results establish a quantitative rule linking replichore asymmetry to fitness, define the termination position under imbalance, and reveal a dual Tus/ter barrier that safeguards termination when one replichore reaches the terminus region ahead of the other. Collectively these results connect chromosome architecture to replication mechanics and cell-division outcomes.

microbiology↗

16S rRNA Modifications Are Dispensable for Viability but Collectively Optimize Ribosome Biogenesis and Translation Initiation in Escherichia coli

Ribosomal RNAs contain numerous conserved nucleotide modifications, yet the functional importance of most of these modifications remains unclear. In Escherichia coli, deletion of individual 16S rRNA modification enzymes generally produces only minor phenotypes, raising questions about their biological significance. Here, we generated a comprehensive collection of deletion mutants lacking individual and combined 16S rRNA modifications, culminating in a strain lacking all known 30S ribosomal subunit modifications. Despite the absence of all known 16S rRNA modifications, cells remained viable, exhibiting a fitness defect of ~30% at 37 {degrees}C that increased to ~50% at 20 {degrees}C, consistent with impaired ribosome biogenesis. We identified strong epistatic interactions between modifications in the 3' major and 3' minor domains of 16S rRNA, resulting in disproportionately large effects on both fitness and antibiotic susceptibility. Live-cell single-molecule tracking revealed a marked increase in the fraction of non-translating ribosomes and a prolonged time required to enter productive translation, whereas translational elongation by actively engaged 70S ribosomes remained largely unaffected. In addition, fluorescence-based measurements showed that unmodified ribosomes exhibited increased stringency during translation initiation, reducing utilization of near-cognate start codons. Together, these findings demonstrate that 16S rRNA modifications are not essential for viability but collectively enhance the efficiency, robustness, and fidelity of ribosome assembly and translational initiation.

microbiology↗

Ribosome collisions trigger tmRNA-mediated rescue through mRNA disengagement

Ribosome rescue pathways maintain translation during normal growth and contribute to stress tolerance. In Escherichia coli, HrpA is a ribosome-collision recognition factor that mediates antibiotic tolerance by resolving stalled ribosomes, yet the fate of the resulting ribosomal intermediate, with a topologically trapped peptidyl-tRNA in the 50S nascent peptide exit tunnel, has remained unclear. Using in vivo single-molecule tracking, we show that under antibiotic treatment, HrpA-dependent collision processing promotes recruitment of tmRNA and Hsp15 as downstream rescue factors. Cryo-electron microscopy of affinity-purified native complexes from tmRNA-deficient cells reveals accumulation of aberrant 70S{middle dot}peptidyl-tRNA complexes and shows that Hsp15, previously thought to interact exclusively with 50S{middle dot}peptidyl-tRNA, binds to 70S complexes lacking mRNA in the E site. This intermediate can be resolved by the canonical tmRNA pathway, thereby connecting HrpA-dependent collision processing to canonical tmRNA-mediated rescue.

molecular biology↗

Recovering membrane interaction kinetics of single molecules from 3D tracking data

Interactions between cytosolic biomolecules and the bacterial inner membrane are fundamental to many cellular processes, yet directly measuring their binding kinetics in living cells remains challenging. Conventional two-dimensional single-molecule tracking analyses can be insufficient, particularly when membrane association does not markedly alter the diffusion rate. Here, we present a method to recover membrane interaction kinetics from three-dimensional single-molecule trajectories in rod-shaped bacteria. Using simulated 3D tracking data, we identify membrane-associated motion by quantifying how well short trajectory segments follow the circular curvature of the cell membrane. The resulting measure is further analyzed using a hidden Markov modeling framework, enabling robust discrimination between cytosolic and membrane-bound states and capturing the dynamics of state transitions without requiring diffusion-rate changes or direct colocalization with membrane markers. This work establishes a general framework for extracting membrane interaction kinetics from 3D single-molecule tracking data in live bacteria, and highlights the value of realistic microscopy simulations for quantitative interpretation and systematic bias assessment.

biophysics↗

Systems-level longitudinal immune profiling reveals individualized immunotypes and genetic associations

The human immune system exhibits substantial inter-individual variation, yet how this variability is coordinated across the system and persists over time remains incompletely defined. Here, we integrated whole-genome sequencing with longitudinal multi-omics immune profiling in 101 healthy individuals followed over two years, combining mass cytometry-based immune cell profiling, PBMC transcriptomics, plasma proteomics, and clinical measurements. Coordinated variation across cellular composition, gene expression, and circulating proteins revealed reproducible immune patterns within individuals across visits. Integrative analyses identified three major immunotypes: adaptive lymphoid (CD4 T cell and B cell enriched), myeloid-inflammation (monocyte and dendritic cell-driven), and cytotoxic (CD8 T cell-dominated), each associated with distinct metabolic and inflammatory profiles. Genome-wide association analyses identified cell-type-specific quantitative trait loci primarily affecting memory lymphocytes, and a polygenic score for memory B cells correlated with both cellular abundance and transcriptional activity. Together, these findings provide a systems-level view for understanding baseline immune heterogeneity in human populations. TeaserLongitudinally stable immune individuality reflects coordinated modules shaped by systemic physiology and genetics

systems biology↗

Human settlement drives population divergence of Leishmania guyanensis at the sylvatic-anthropogenic interface

The Amazon is the largest tropical rainforest in the world, and is an important interface between natural rainforest and anthropogenic environments. In the central Amazon region, Leishmania guyanensis is a common agent of cutaneous leishmaniasis (CL), a disfiguring vector-borne disease that circulates between wild mammalian reservoirs and sandfly vectors in sylvatic cycles. People become infected when they interact or disturb this cycle. At present, little is known about the adaptation of Leishmania parasites at the interface between sylvatic and anthropogenic environments. Here, to characterize CL in a transition zone, we conducted a population genomic analysis of 77 Leishmania isolates from human patients living in Manauss outskirts or in highways adjacent to the Brazilian Amazon rainforest. We discovered two L. guyanensis populations; an ancestral sylvatic population occurring in densely forested areas far from the city and a derived population, occurring in areas with lower forest cover closer to the city (the city-proximal population). The genomic divergence between these populations indicates that their separation occurred relatively recently, consistent with human-driven development of a new transmission cycle for CL in the Manaus region. The lack of recent gene flow between these populations indicates that the newer city-proximal population has established a transmission cycle that operates independently from ancestral sylvatic transmission. This provides a striking example of the adaptation of disease transmission to an environment that has been altered by human activity. Significance StatementEmerging infectious diseases often arise at the interface between natural and human-modified environments. One of these important interfaces is the Amazon, the largest rainforest in the world. We analyzed the population genomics of Leishmania guyanensis, the agent of cutaneous leishmaniasis in the Amazon, typically transmitted in sylvatic cycles. Our findings reveal a recent emergence of a L. guyanensis population associated with anthropogenic environment alterations, which is genetically distinct from the ancestral sylvatic lineage. This newly adapted population highlights how landscape change and urban expansion may drive the evolution and emergence of protozoan vector-borne pathogens at the rainforest-urban interface.

evolutionary biology↗

Evaluating Field Trial Designs for Genetically Modified Mosquito Interventions: An In-Silico Simulation Approach

Mosquito control strategies based on the mass release of modified males, such as genetically modified mosquitoes (GMMs), aim to suppress wild populations by impairing reproduction. Evaluating these interventions requires resource-intensive field trials, but a lack of standardized implementation practices, particularly regarding release ratios of modified males to wild female mosquitoes and trial timing, has led to variable outcomes. This studys objective is to propose a modeling tool for the "in-silico" simulation of trial designs before field implementation. To this aim, we developed an agent-based model of mosquito population dynamics. As a case study, we calibrated the model using 2019-2023 Aedes aegypti surveillance data from Miami-Dade County, Florida, and compared two GMM trial designs as illustrative examples. Our results show that depending on the implementation choices (e.g., trial start date and duration, release ratio), trials yield highly variable outcomes. For example, changing the start date while fixing all other implementation details can lead to effectiveness between 50% and 90%. Our findings suggest that "in-silico" simulation is a valuable tool for improving trial protocol design, allowing stakeholders to test strategies and reduce outcome uncertainty before committing to a fieldwork experiment.

ecology↗

Inference over hidden contexts shapes the geometry of conceptual knowledge for flexible behaviour

Flexible decision-making in uncertain environments requires inferring latent task states and prioritising behaviourally relevant information. We tested the hypothesis that this process is associated with systematic changes in attention and neural representational structure. Human participants performed a serial reversal learning task that required identifying which stimulus dimension(s) were currently relevant. Behaviourally, participants rapidly adapted to context switches, and a hidden-state inference model best explained these adaptations, outperforming multiple reinforcement-learning variants in predicting both choices and inferred contexts. Oculomotor behaviour provided converging evidence for selective attention, with gaze increasingly concentrated on relevant features, tracking trial-by-trial belief updates, and transiently broadening following high prediction errors. Guided by these results, we used magnetoencephalography to examine how latent-state inference was reflected in neural activity. Neural representations encoded the currently inferred context and were modulated by recent prediction errors, with larger prediction errors associated with weaker context representations on subsequent trials. Representational analyses further revealed that successful performance was associated with a systematic reorganisation of neural geometry, characterised by selective amplification of task-relevant representational distances and increased separation of behaviourally relevant stimulus values. Together, our findings demonstrate that latent-state inference, attention, and neural representational geometry are tightly coordinated during flexible decision-making, providing a systems-level account of how behaviourally relevant information is selectively prioritised and represented to support adaptive behaviour.

neuroscience↗

Comprehensive evaluation of antibody responses to mosquitoes and mosquito-borne arboviruses using highly multiplexed serology

Vector-borne diseases (VBDs) are a leading cause of morbidity and mortality worldwide, and their future impact may increase due to climate change. Antigens driving host immune responses to pathogens and vectors can serve as vaccine candidates and biomarkers of previous exposure, but the immunogenicity of the relevant proteomes remains undercharacterized. To comprehensively profile antibody responses to VBDs and to the vectors themselves, we developed a highly multiplexed phage display library (VectorScan) containing over 250,000 peptides derived from diverse arthropod vectors and prevalent vector-borne pathogens. We used phage immunoprecipitation sequencing (PhIP-Seq) to screen VectorScan against blood samples from non-human primates and humans with experimental and natural exposures to arboviruses and mosquitoes (Anopheles gambiae and Aedes aegypti). We analyzed quantitative measurements of peptide seroreactivity to identify epitopes driving viral serotype-level exposure signatures, as well as novel mosquito sialome antigens. Mosquito-directed antibody responses were associated with natural viral exposure, but highly heterogeneous at an individual level. However, recurrent responses to insect-derived cuticular proteins, mucin-like proteins, and fibrinogen-like proteins will inform development of future serosurveillance tools for vector exposure and vector-based vaccines.

immunology↗

Prior knowledge influences the neural mechanisms supporting memory-based inference

Memory allows us to go beyond direct experience by enabling inference across separate events. Yet how prior knowledge shapes the mechanisms supporting such inferences remains unclear. Here, we show that alignment between ongoing experience and existing knowledge determines how the brain constructs cross-event inferences. Behaviorally, inferences across schema-congruent events did not require accurate retrieval of the individual episodes, whereas inferences across schema-incongruent events depended on successful retrieval of those episodes. EEG-based multivariate pattern analysis further showed that schema-congruent inference was supported by schema reinstatement during encoding, consistent with the formation of integrated memory representations. In contrast, schema-incongruent inference relied on context-specific reinstatement at retrieval, reflecting the flexible recombination of distinct memories. Together, these findings demonstrate that prior knowledge dynamically shapes memory toward integration or separation, revealing how the brain constructs inferences to support flexible cognition.

neuroscience↗

Integrating Cell Painting and Thermal Proteome Profiling for Inference of Targets and Mechanism of Action

Understanding the mechanism of action (MoA) of bioactive compounds is a central challenge in drug discovery and chemical biology. In this article, we propose a strategy for integrating morphological data with proteomics to provide deeper insights into the MoA of compounds. We combine the rich phenotypic profiles of Cell Painting (CP) with the unbiased protein target detection of Thermal Proteome Profiling (TPP), and construct protein-protein interaction networks based on potential targets identified using both assays. We evaluate our method on public TPP data sets for the five compounds (+)-JQ1, I-BET151, Vemurafenib, Crizotinib and Panobinostat, together with Cell Painting data for 5270 drugs on U2OS cells. We show that the combined approach could accurately identify known MoAs for four out of five compounds. This work highlights the value of multimodal profiling for chemical biology and opens new avenues for data-driven discovery of therapeutic mechanisms.

bioinformatics↗

Episodic events are flexibility encoded in both integrated and separated neural representations

This study investigates how the brain encodes episodic events to support diverse memory functions. Thirty-six participants viewed movies simulating real-life interactions while EEG was recorded. They first watched movies featuring two characters (AB), followed by scenes where one original character interacted with a new one (BC). Memory was assessed for direct (AB and BC), indirect (AC) associations, and source memory about whether two characters had appeared together. Source memory was more accurate when participants correctly inferred indirect associations. Time-resolved representational similarity analysis (RSA) of EEG data revealed both neural pattern similarities and dissimilarities during AB and BC encoding. Pattern similarities, indicating memory integration across episodes, predicted successful indirect associations. Conversely, dissimilarities predicted accurate source memory, indicating the preservation of distinct memory traces. These findings suggest that episodic events are flexibly encoded through both integrated and segregated neural representations, enabling the brain to support multiple memory functions depending on task demands.

neuroscience↗

Neural dynamics of relational memory retrieval across eye movements

Relational memory retrieval entails a dynamic interplay between eye movements and neural activity, yet the temporal coordination of these processes remains unclear. This study examines the relationship between theta and alpha EEG activity and sequential fixations during relational memory retrieval. Participants completed a two-alternative forced-choice associative memory task while their eye movements and EEG were recorded simultaneously. Results reveal a relational eye-movement effect: correctly remembered target elements were disproportionately fixated during later stages of the retrieval sequence. Time-frequency EEG analyses across the test trial show that successful retrieval was characterized by an early, transient increase in theta power and a sustained decrease in alpha power. Gaze-related analyses linked these effects to distinct retrieval processes. The theta effect emerged after the initial cue fixation and predicted retrieval success independently of whether subsequent saccades were directed to the target or distractor. In contrast, the alpha decrease predicted retrieval success only across continued target fixations. These findings connect the relational eye-movement effect to two distinct neural signatures: a nonspecific theta increase, reflecting recollection that may initiate pattern completion, and a target-selective alpha decrease, reflecting sustained reactivation of goal-relevant associations across fixations. Together, these results clarify the temporal dynamics of relational memory retrieval and underscore the role of sequential eye movements in memory-guided behavior.

neuroscience↗

Resting-State EEG Aperiodic Exponent Moderates the Association Between Age and Memory Performance in Older Adults

Memory functions are susceptible to age-related cognitive decline, making it essential to explore the underlying neurophysiological mechanisms that contribute to memory function during healthy ageing. Resting-state EEG (rsEEG) parameters, particularly the aperiodic exponent, a marker of cortical excitation-inhibition balance, and individual alpha peak frequency, a correlate of neural processing efficiency, have demonstrated associations with ageing and cognitive functions. This study investigated associations between these rsEEG markers and performance across multiple memory systems in healthy older adults (n = 99) aged 50-84 years, specifically the direct associations of these markers on memory across episodic, working, and visual short-term memory systems, assessed via computerised tasks, as well as their moderating effects on age-memory relationships. While no direct associations were seen between rsEEG markers and memory performance across tasks beyond the contribution of age, gender and education, results revealed significant moderating effects of the aperiodic exponent on age-related performance in episodic and visual short-term memory. Notably, for individuals with a higher exponent, age was not significantly associated with episodic or visual short-term memory performance, whereas those with average and lower exponent values showed poorer performance with older age. These findings suggest that average and lower aperiodic exponents may reflect a marker of decrement in age-related memory performance and higher exponents may index an underlying protective mechanism against age-related memory decline. This investigation extends the current understanding of cognitive ageing mechanisms by identifying the aperiodic exponent as a potential biomarker explaining individual differences in cognitive ageing trajectories in older adult populations, particularly in episodic and visual short-term memory systems, and establishes a framework for studying neuroprotective mechanisms and developing interventions to preserve cognitive function in older adults.

neuroscience↗

High-content morphological profiling by Cell Painting in 3D spheroids.

Cell Painting is a popular assay for morphological profiling of multi-labeled 2D monolayer cell cultures used in a wide range of applications. Culturing cells in 3D has potential for higher physiological relevance, such as when studying effects of perturbations. Robust and scalable 3D models can be challenging to characterize through imaging - particularly because light has difficulty penetrating cell multilayers. We introduce a scalable method where the Cell Painting assay is combined with tissue-clearing and applied to 3D spheroids generated in a ULA microplate format. Multi-channel images are acquired using confocal microscopy, and cells can be segmented inside those spheroids allowing for relevant morphological features to be extracted. Our end-to-end analysis pipeline comprises cell segmentation, morphological feature extraction, and between-spheroids and within-spheroid normalization. We demonstrate the method using spheroids cultured from two colorectal cancer cell lines and successfully detect distinct phenotypic changes upon compound treatments, on both spheroid-level using maximum intensity projections and on single cell-level. We show that drugs group by mechanism of action, with biologically relevant clusters especially evident with single-cell data. Finally, we contrast our method to results from 2D Cell Painting and discover a different pattern in DNA damaging drugs in HCT116 colorectal cancer cells. This work lays the foundation for multi-channel image-based screening in 3D spheroids.

pharmacology and toxicology↗

Leveraging Neural Network Models for Drug Repurposing: A case study on Cardiac Hypertrophy

BackgroundDrug repurposing has emerged as an attractive strategy in contemporary pharmaceutical research, presenting an opportunity to expedite drug discovery, minimize developmental costs, and mitigate risks associated with developing new pharmaceuticals. In this study, we investigated a novel approach based on deep learning of human transcriptomic mechanisms for systematic identification of additional therapeutic potential in preexisting drugs. MethodWe trained a composite feedforward neural network model using gene expression data sourced from the ARCHS4 compilation of the GEO, encompassing extensive human datasets. Subsequently, disease-associated gene expression data were generated from our stem cell-derived in vitro model of cardiac hypertrophy induced by Endothelin-1 stimulation. These data were employed to identify latent variables associated with genes showing differential expression due to Endothelin-1 stimulation. By examining the differential expression profiles within the models latent space, we successfully correlated the disease signal with known drug targets found in pharmaceutical compounds cataloged in DrugBank. ResultsThe model accurately encoded additional disease-related genes beyond the curated gene set, demonstrating its ability to generalize disease associations. Leveraging the model, we identified potential drug candidates, such as lapatinib and amiodarone showing promise in mitigating proBNP concentration associated with cardiac hypertrophy. ConclusionThis study demonstrates the power of deep learning of human transcriptomic mechanisms in swiftly identifying new therapeutic potentials for existing drugs, highlighting the pivotal role of artificial intelligence technologies in accelerating drug development for other complex medical conditions. HighlightsO_LIAn interpretable neural network model for identification of candidate drugs for drug repurposing C_LIO_LIEncoding expression data from our cardiac hypertrophy model highlights important disease mechanisms C_LIO_LILapatinib and amiodarone experimentally validated as candidate drugs for cardiac hypertrophy therapies C_LI

bioinformatics↗

Histone H3 tail modifications required for meiosis in Saccharomyces cerevisiae

Histone tail phosphorylation has diverse effects on a myriad of cellular processes, including cell division, and is highly conserved throughout eukaryotes. Histone H3 phosphorylation at threonine 3 (H3T3) during mitosis occurs at the inner centromeres and is required for proper biorientation of chromosomes on the mitotic spindle. While H3T3 is also phosphorylated during meiosis, a possible role for this modification has not been tested. Here, we asked if H3T3 phosphorylation (H3T3ph) is important for meiotic division by quantifying sporulation efficiency and spore viability in Saccharomyces cerevisiae mutants with a T3A amino acid substitution. The T3A substitution resulted in greatly reduced sporulation efficiency and reduced spore viability. Analysis of two other H3 tail mutants, K4A and S10A, revealed different effects on sporulation efficiency and spore viability compared to the T3A mutant, suggesting that these phenotypes are due to failures in distinct functions. To determine if the spindle checkpoint promotes spore viability of the T3A mutant, the MAD2 gene required for the spindle assembly checkpoint was deleted to abolish spindle assembly checkpoint function. This resulted in a severe reduction in spore viability following meiosis. Altogether, the data reveal a critical function for histone H3 threonine 3 that requires monitoring by the spindle checkpoint to ensure successful completion of meiosis.

genetics↗