Biology subjects

Brenner, M.

Publications and source records attributed to Brenner, M..

17 recordsLinked to original sources

bioRxiv2026

Protein Language Models: Learning From Evolution, Designing Beyond It

Protein language models (PLMs) have transformed our ability to learn from evolutionary sequence space, but protein engineering ultimately asks a different question: not what evolution selected, but what we should build next. Zero-shot likelihoods therefore provide useful, but not universal, measures of fitness and can misalign with engineering objectives. Experimental supervision redirects these priors towards properties of interest, enabling target-specific prediction and closed-loop optimization. PLMs thereby complement structure-based design: structural methods provide geometric control, while PLMs integrate experimental feedback to optimize functional and developability properties. Realizing this potential requires evaluation beyond retrospective predictive accuracy, towards extrapolation, multi-objective optimization, and prospective experimental success. We argue for recurring competitions combining scalable predictive benchmarks with prospective experimental challenges. Ultimately, progress should be measured not by predicting existing experiments, but by enabling successful new ones.

bioinformatics↗

bioRxiv2026

DeePEn - A Depth sensitive benchmark for Protein Engineering

Recent progress in modeling techniques and high-throughput screening has significantly enhanced the accessibility of protein engineering. Nevertheless, further progress gets hindered by the lack of robust benchmarks that capture the practical challenges for real-world protein engineering. Here, we introduced DeePEn, a Depth-sensitive benchmark for Protein Engineering that quantifies a models generalization capabilities when predicting protein fitness at increasing mutational distance from the wildtype or training data. We defined distance as the number of simultaneous point mutations, i.e., single amino acid variants (SAVs), moving from wild-type to mutant (edit distance in computer science jargon). Specifically selecting four deep mutational scanning (DMS) datasets with sufficient multi-mutation data points from ProteinGym, we assessed recent predictive models, including general and biophysics-informed protein Language Models (pLMs), and a non-transformer neural network. Our results highlight how the performance of all models deteriorates with increasing mutational distance and that no single metric sufficiently captures the diverse requirements of protein engineering. To overcome these shortcomings, DeePEn provides a readily available resource for multi-metric benchmarking that focuses on the prediction of distant variants.

bioinformatics↗

bioRxiv2026

Pseudoperplexity Probes Memorization in Protein Language Models

Protein Language Models (pLMs) have significantly advanced computational biology. Yet their scale and reliance on redundant training data raise a fundamental question: do pLMs generalize the statistical grammar of proteins, or do they simply memorize their training data? To investigate this, we used pseudoperplexity as a probe for sequence-level memorization, comparing ProtT5's pseudoperplexity on a pre-training proxy dataset against a post-training holdout of genuinely novel sequences. To ensure a valid comparison, we matched the datasets by sequence length, cluster size, and taxonomic family. As a statistical baseline, we trained n-gram language models; analysis of higher-order n-gram composition and a statistically significant divergence in perplexity confirmed that the post-training sequences were genuinely novel at the local sequence level. ProtT5 showed a statistically significant difference in pseudoperplexity between seen and unseen sequences, though further analysis revealed this memorization signal to be modest. These findings suggest that ProtT5 exhibits detectable but limited memorization of its training data as measured by a pseudoperplexity-based probe.

bioinformatics↗

bioRxiv2026

Interpretable Hierarchical RNNs for rs-fMRI: Promise and Limits of Individualized Brain Dynamics

Modeling individual brain dynamics from resting-state fMRI (rs-fMRI) remains challenging due to substantial inter-subject variability, noise, and limited data length per subject. Here, we systematically evaluate whether hierarchical shallow piecewise-linear recurrent neural networks (shPLRNNs), recently introduced as interpretable dynamical system reconstruction models, can generate individualized rs-fMRI time series while preserving subject-specific functional connectivity structure. We applied the framework to 1,423 rs-fMRI samples from healthy participants of the Marburg-Munster Affective Disorders Cohort Study (MACS). Simulated rs-fMRI data reproduced substantial empirical FC structure, with comparable reconstruction accuracy on the validation and held-out test sets. Generalization to unseen individuals was heterogeneous and strongly depended on how typical a subjects connectivity pattern was relative to the training cohort, with template similarity explaining 37% of variance in reconstruction accuracy. Learned subject-specific parameters exhibited significant test-retest stability and higher within-subject than between-subject similarity on longitudinal data from two different timepoints, supporting their interpretation as individualized dynamical markers. Associations between individual parameters and demographic or cognitive variables were statistically significant but modest in effect size, and predictive performance remained below that obtained using empirical rs-fMRI features directly. Empirical FC was used as a reference for static subject information rather than as a target to be outperformed. Together, these results suggest that hierarchical shPLRNNs can extract meaningful and partially stable individual-specific dynamical structure from rs-fMRI data. The findings delineate key trade-offs between model expressivity, generalization and subject specificity, and point to directions for future methodological refinement in individualized brain modeling. Graphical AbstractA hierarchical dynamical RNN captures substantial individual rs-fMRI functional connectivity structure using compact subject-specific parameters embedded in shared population dynamics. The resulting representations generalize to held-out subjects and show test-retest stability, but only modest associations with phenotypic variables. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=150 SRC="FIGDIR/small/713153v3_ufig1.gif" ALT="Figure 1"> View larger version (61K): org.highwire.dtl.DTLVardef@1b029edorg.highwire.dtl.DTLVardef@90bdd6org.highwire.dtl.DTLVardef@9f6b77org.highwire.dtl.DTLVardef@486c9e_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2024

Information-making processes in the speaker's brain drive human conversations forward

Natural human conversation is driven by the exchange of information-rich messages that surprise the listener and deviate from predictable context. While extensive research has characterized how the brain processes unexpected linguistic input during comprehension, the neural mechanisms underlying the generation of such information by the speaker remain poorly understood. In this study, we hypothesize that the speakers brain actively creates information through extra neural computation, transforming internal thoughts into novel, meaningful linguistic outputs. Utilizing a unique 24/7 dataset of continuous electrocorticography (ECoG) comprising approximately 100 hours of spontaneous natural conversations, we contrast the neural basis of speech production and comprehension within the same participants. Behaviorally, we find that speakers take longer pausing for an additional 100-150 milliseconds before producing information-rich, improbable words, even when controlling for word identity and frequency. Neurally, we provide converging evidence for a previously unreported process: generating information-rich words elicits significantly stronger neural activity (ERP) and enhanced neural encoding and decoding in language-related areas starting 100-500 ms before articulation. This pattern contrasts sharply with speech comprehension, where enhanced neural activity for predictable words occurs before onset, while responses to improbable words emerge only after onset as prediction errors. Furthermore, we demonstrate that large language models (LLMs) mirror this biological process, requiring deeper internal computation across layers to generate improbable versus probable words. Together, these results reveal that the speakers brain is not merely a transmission channel but an active generator of information, recruiting extra neural resources to create novel content that diverges from listener expectations.

neuroscience↗

bioRxiv2024

Enhancing Photosynthesis under Salt Stress via Directed Evolution in Cyanobacteria

A key aspect of enhancing photosynthesis is improving its recycling kinetics, enabling swift resumption of photochemical quenching following environmental disruptions or stress. Salt stress exacerbates high light stress in cyanobacteria and leads to severe yield losses in crop plants. Genetic traits that confer salt tolerance without compromising photosynthetic performance are essential for improving photosynthesis under these conditions. Here we applied accelerated evolution in Synechococcus elongatus PCC 7942 by conditionally suppressing its methyl-directed mismatch repair system to obtain beneficial genetic traits for enhanced photosynthesis under salt stress. Screening over 10,000 mutants, we isolated eight strains with increased biomass or sucrose productivity under salt stress. Genome sequencing revealed an average of 8-20 single nucleotide polymorphisms (SNPs) or indels per genome. Notably, mutations in the photosystem II (PSII) reaction center D1 gene, resulting in the amino acid changes L353F, I358N, and H359N at the carboxyl terminus of the pre-D1 (pD1) protein, improve photosynthesis under salt and combined salt and light stress by potentially accelerating D1 maturation during PSII repair. Phylogenetic analysis of pD1 across cyanobacteria and red algae highlights the broad significance of these adaptive genetic traits, underscoring the importance of leveraging evolutionary insights to improve photosynthesis under stress or fluctuating environments.

plant biology↗

bioRxiv2023

Deep speech-to-text models capture the neural basis of spontaneous speech in everyday conversations

Humans effortlessly use the continuous acoustics of speech to communicate rich linguistic meaning during everyday conversations. In this study, we leverage 100 hours (half a million words) of spontaneous open-ended conversations and concurrent high-quality neural activity recorded using electrocorticography (ECoG) to decipher the neural basis of real-world speech production and comprehension. Employing a deep multimodal speech-to-text model named Whisper, we develop encoding models capable of accurately predicting neural responses to both acoustic and semantic aspects of speech. Our encoding models achieved high accuracy in predicting neural responses in hundreds of thousands of words across many hours of left-out recordings. We uncover a distributed cortical hierarchy for speech and language processing, with sensory and motor regions encoding acoustic features of speech and higher-level language areas encoding syntactic and semantic information. Many electrodes--including those in both perceptual and motor areas--display mixed selectivity for both speech and linguistic features. Notably, our encoding model reveals a temporal progression from language-to-speech encoding before word onset during speech production and from speech-to-language encoding following word articulation during speech comprehension. This study offers a comprehensive account of the unfolding neural responses during fully natural, unbounded daily conversations. By leveraging a multimodal deep speech recognition model, we highlight the power of deep learning for unraveling the neural mechanisms of language processing in real-world contexts.

neuroscience↗

bioRxiv2023

A longitudinal single-cell therapeutic atlas of anti-tumour necrosis factor treatment in inflammatory bowel disease

Precision medicine in immune-mediated inflammatory diseases (IMIDs) requires an understanding of how cellular networks change following therapy. We describe a therapeutic atlas for Crohns disease (CD) and ulcerative colitis (UC) following anti-tumour necrosis factor (TNF) therapy. We generated ~1 million single-cell transcriptomes, organised into 109 cell states, from 216 gut biopsies from 38 patients and three controls, revealing disease- and therapy-specific differences. A systems-biology analysis identified distinct spatially-resolved cellular microenvironments: granuloma signatures in CD and interferon (IFN)-response signatures localising to T-cell aggregates and epithelial damage in CD and UC. Longitudinal comparisons demonstrated that disease progression in non-responders associated with myeloid and stromal cell perturbations in CD and increased multi-cellular IFN signalling in UC. IFN signalling was also observed in rheumatoid arthritis (RA) synovium with a lymphoid pathotype. Our therapeutic atlas informs drug positioning across IMIDs, and suggests a rationale for the use of janus kinase (JAK) inhibition following anti-TNF resistance.

immunology↗

bioRxiv2022

Correspondence between the layered structure of deep language models and temporal structure of natural language processing in the human brain

Deep language models (DLMs) provide a novel computational paradigm for how the brain processes natural language. Unlike symbolic, rule-based models described in psycholinguistics, DLMs encode words and their context as continuous numerical vectors. These "embeddings" are constructed by a sequence of computations organized in "layers" to ultimately capture surprisingly sophisticated representations of linguistic structures. How does this layered hierarchy map onto the human brain during natural language comprehension? In this study, we used electrocorticography (ECoG) to record neural activity in language areas along the superior temporal gyrus and inferior frontal gyrus while human participants listened to a 30-minute spoken narrative. We supplied this same narrative to a high-performing DLM (GPT2-XL) and extracted the contextual embeddings for each word in the story across all 48 layers of the model. We next trained a set of linear encoding models to predict the temporally-evolving neural activity from the embeddings at each layer. We found a striking correspondence between the layer-by-layer sequence of embeddings from GPT2-XL and the temporal sequence of neural activity in language areas. In addition, we found evidence for the gradual accumulation of recurrent information along the linguistic processing hierarchy. However, we also noticed additional neural processes in the brain, but not in DLMs, during the processing of surprising (unpredictable) words. These findings point to a connection between human language processing and DLMs where the layer-by-layer accumulation of contextual information in DLM embeddings matches the temporal dynamics of neural activity in high-order language areas.

neuroscience↗

bioRxiv2022

Incubation of palatable food craving is associated with brain-wide neuronal activation in mice

Studies using rodent models have shown that relapse to drug or food seeking increases progressively during abstinence, a phenomenon termed incubation of craving. Mechanistic studies of incubation of craving have focused on specific neurobiological targets within pre- selected brain areas. Recent methodological advances in whole-brain immunohistochemistry, clearing, and imaging now enable unbiased brain-wide cellular resolution mapping of regions and circuits engaged during learned behaviors. However, these whole brain imaging approaches were developed for mouse brains while incubation of drug craving has primarily been studied in rats and incubation of food craving has not been demonstrated in mice. Here, we established a mouse model of incubation of palatable food craving and examined food reward seeking after 1, 15, and 60 abstinence days. We then used the neuronal activity marker Fos with intact brain mapping procedures to identify corresponding patterns of brain-wide activation. Relapse to food seeking was significantly higher after 60 abstinence days than after 1 or 15 days. Using unbiased ClearMap analysis, we identified increased activation of multiple brain regions, particularly corticostriatal structures, following 60, but not 15 abstinence days. We used orthogonal SMART2 analysis to confirm these findings within corticostriatal and thalamocortical subvolumes and applied expert-guided registration to investigate subdivision and layer-specific activation patterns. Overall, we (1) identified novel brain-wide activity patterns during incubation of food seeking using complementary analytical approaches, and (2) provide a single-cell resolution whole-brain atlas that can be used to identify functional networks and global architecture underlying incubation of food craving. Significance StatementRelapse to reward seeking progressively increases during abstinence, a phenomenon termed incubation of craving. Mechanistic studies of incubation can lead to novel relapse treatments. However, previous studies have primarily used rat models and targeted region-by-region analyses and a brain-wide functional atlas of incubation of reward seeking is lacking. We established a behavioral procedure for incubation of palatable food seeking in mice and applied whole-brain activity mapping with Fos as a neuronal activity marker to identify the functional connectome of this incubation. Like rats, mice showed incubation of food seeking during abstinence. Using two complementary activity mapping approaches, we identified a brain-wide pattern of increased neural activation that mirrored incubation of food seeking after 60, but not 15, days of abstinence.

neuroscience↗

bioRxiv2021

Listeria monocytogenes TcyKLMN cystine/cysteine transporter facilitates glutathione synthesis and virulence gene expression

Listeria monocytogenes (Lm) is a saprophyte and a human intracellular pathogen. Upon invasion into mammalian cells, it senses multiple metabolic and environmental signals that collectively trigger its transition to the pathogenic state. One of these signals is the tripeptide glutathione, which acts as an allosteric activator of Lms master virulence regulator, PrfA. While glutathione synthesis by Lm was shown to be critical for PrfA activation and virulence gene expression, it remains unclear how this tripeptide is synthesized under changing environments, especially in light of the observation that Lm is auxotrophic to one of its precursors, cysteine. Here, we show that the ABC transporter TcyKLMN is a cystine/cysteine importer that supplies cysteine for glutathione synthesis, hence mediating the induction of the virulence genes. Further, we demonstrate that this transporter is negatively regulated by three metabolic regulators: CodY, CymR and CysK, which sense and respond to changing concentrations of branched chain amino acids (BCAA) and cysteine. The data indicate that under low concentrations of BCAA, TcyKLMN is up-regulated, driving the production of glutathione by supplying cysteine, thereby facilitating PrfA activation. These findings provide molecular insight into the coupling of Lm metabolism and virulence, connecting BCAA sensing to cysteine uptake and glutathione biosynthesis, as a mechanism that controls virulence gene expression. This study exemplifies how bacterial pathogens sense their intracellular environment and exploit essential metabolites as effectors of virulence. ImportanceBacterial pathogens sense the repertoire of metabolites in the mammalian niche and use this information to shift into a pathogenic state to accomplish successful infection. Glutathione is a virulence-activating signal that is synthesized by L. monocytogenes during infection of mammalian cells. In this study, we show that cysteine uptake via TcyKLMN drives glutathione synthesis and virulence gene expression. The data emphasize the intimate cross-regulation between metabolism and virulence in bacterial pathogens.

microbiology↗

bioRxiv2020

Thinking ahead: prediction in context as a keystone of language in humans and machines

Departing from traditional linguistic models, advances in deep learning have resulted in a new type of predictive (autoregressive) deep language models (DLMs). Using a self-supervised next-word prediction task, these models are trained to generate appropriate linguistic responses in a given context. We provide empirical evidence that the human brain and autoregressive DLMs share three fundamental computational principles as they process natural language: 1) both are engaged in continuous next-word prediction before word-onset; 2) both match their pre-onset predictions to the incoming word to calculate post-onset surprise (i.e., prediction error signals); 3) both represent words as a function of the previous context. In support of these three principles, our findings indicate that: a) the neural activity before word-onset contains context-dependent predictive information about forthcoming words, even hundreds of milliseconds before the words are perceived; b) the neural activity after word-onset reflects the surprise level and prediction error; and c) autoregressive DLM contextual embeddings capture the neural representation of context-specific word meaning better than arbitrary or static semantic embeddings. Together, our findings suggest that autoregressive DLMs provide a novel and biologically feasible computational framework for studying the neural basis of language.

neuroscience↗

bioRxiv2020

Abuse liability, antinociceptive, and discriminative stimulus properties of IBNtxA

ABSTRACTRationale IBNtxA (3-iodobenzoyl naltrexamine) is a novel μ opioid receptor (MOR) agonist structurally related to the classical MOR antagonist naltrexone. Recent studies suggest IBNtxA preferentially signals through truncated MOR splice variants, producing a unique pharmacological profile resulting in antinociception with reduced side effects, including no conditioned place preference (CPP) when tested at a single dose. IBNtxA represents an intriguing lead compound for preclinical drug development targeting truncated MOR splice variants but further evaluation of its in vivo pharmacological profileObjective The purpose of this study was to independently verify the analgesic properties of IBNtxA and to examine more completely the rewarding properties and discriminative stimulus effects of IBNtxA in order to more completely understand the in vivo effects of IBNtxA and further assess evaluate its abuse liability.Results IBNtxA was synthesized and compared to morphine in variety of mouse behavioral assays. 3 mg/kg IBNtxA was equipotent to 10 mg/kg morphine in a hot plate analgesia assay. In drug discrimination testing using mice trained to discriminate between 3 mg/kg IBNtxA and DMSO/saline vehicle, the κ agonist U-50488 fully substituted for IBNtxA. Classical μ agonist morphine, δ agonist SNC162, NOP agonist SCH 221510, and μ/NOP partial agonist buprenorphine each partially substituted for IBNtxA. IBNtxA up to 3 mg/kg did not produce a place preference in CPP. Pretreatment with 3 mg/kg IBNtxA but not 1 mg/kg IBNtxA attenuated acquisition of place preference for 10 mg/kg morphine. 3 mg/kg IBNtxA attenuated morphine-induced hyperlocomotion but did not alter naloxone-precipitated morphine withdrawal.Conclusions Overall IBNtxA has a complicated opioid receptor pharmacology in vivo. These results indicate that IBNtxA produces potent antinociception and has low abuse liability, likely driven by substantial κ agonist signaling effects.Competing Interest StatementThe authors have declared no competing interest.View Full Text

pharmacology and toxicology↗

bioRxiv2019

Type II Alexander disease caused by splicing errors and aberrant overexpression of an uncharacterized GFAP isoform.

Alexander disease results from gain of function mutations in the gene encoding glial fibrillary acidic protein (GFAP), an intermediate filament protein expressed in astrocytes. At least eight GFAP isoforms have been described, however, the predominant alpha isoform accounts for approximately 90% of GFAP protein in the central nervous system. Here we describe exonic variants identified in three unrelated families with Type II Alexander disease that alter the splicing of GFAP pre-mRNA and result in upregulation of a previously uncharacterized GFAP lambda isoform (NM 001363846.1). Affected members of Family 1 and Family 2 shared the same missense variant, NM 001363846.1:c.1289G>A;p.(Arg430His) while in Family 3 we identified a synonymous variant in the adjacent nucleotide, NM 001363846.1:c.1290C>A;p.(Arg430Arg). Using RNA and protein analysis of brain autopsy samples, and a mini-gene splicing reporter assay, we demonstrate both variants result in upregulation of the lambda isoform. We assessed other GFAP variants in the ClinVar database for predicted aberrant splicing and using the same assay demonstrated significant changes to splicing for two selected variants. Our approach demonstrates the importance of characterizing the effect of GFAP variants on mRNA splicing in order to inform future pathophysiologic and therapeutic study for Alexander disease.

genetics↗

bioRxiv2018

Fast, sensitive, and flexible integration of single cell data with Harmony

The rapidly emerging diversity of single cell RNAseq datasets allows us to characterize the transcriptional behavior of cell types across a wide variety of biological and clinical conditions. With this comprehensive breadth comes a major analytical challenge. The same cell type across tissues, from different donors, or in different disease states, may appear to express different genes. A joint analysis of multiple datasets requires the integration of cells across diverse conditions. This is particularly challenging when datasets are assayed with different technologies in which real biological differences are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Unlike available single-cell integration methods, Harmony can simultaneously account for multiple experimental and biological factors. We develop objective metrics to evaluate the quality of data integration. In four separate analyses, we demonstrate the superior performance of Harmony to four single-cell-specific integration algorithms. Moreover, we show that Harmony requires dramatically fewer computational resources. It is the only available algorithm that makes the integration of[~] 106 cells feasible on a personal computer. We demonstrate that Harmony identifies both broad populations and fine-grained subpopulations of PBMCs from datasets with large experimental differences. In a meta-analysis of 14,746 cells from 5 studies of human pancreatic islet cells, Harmony accounts for variation among technologies and donors to successfully align several rare subpopulations. In the resulting integrated embedding, we identify a previously unidentified population of potentially dysfunctional alpha islet cells, enriched for genes active in the Endoplasmic Reticulum (ER) stress response. The abundance of these alpha cells correlates across donors with the proportion of dysfunctional beta cells also enriched in ER stress response genes. Harmony is a fast and flexible general purpose integration algorithm that enables the identification of shared fine-grained subpopulations across a variety of experimental and biological conditions.

bioinformatics↗

bioRxiv2018

Pathologically distinct fibroblast subsets drive inflammation and tissue damage in arthritis

The identification of lymphocyte subsets with non-overlapping effector functions has been pivotal to the development of targeted therapies in immune mediated inflammatory diseases (IMIDs). However it remains unclear whether fibroblast subclasses with non-overlapping functions also exist and are responsible for the wide variety of tissue driven processes observed in IMIDs such as inflammation and damage. Here we identify and describe the biology of distinct subsets of fibroblasts responsible for mediating either inflammation or tissue damage in arthritis. We show that deletion of FAP+ synovial cells suppressed both inflammation and bone erosions in murine models of resolving and persistent arthritis. Single cell transcriptional analysis identified two distinct fibroblast subsets: FAP+ THY1+ immune effector fibroblasts located in the synovial sub-lining, and FAP+ THY1- destructive fibroblasts restricted to the synovial lining. When adoptively transferred into the joint, FAP + THY1- fibroblasts selectively mediate bone and cartilage damage with little effect on inflammation whereas transfer of FAP + THY1+ fibroblasts resulted in a more severe and persistent inflammatory arthritis, with minimal effect on bone and cartilage. Our findings describing anatomically discrete, functionally distinct fibroblast subsets with non-overlapping functions have important implications for cell based therapies aimed at modulating inflammation and tissue damage.

immunology↗

bioRxiv2018

A genome-wide innateness gradient defines the functional state of human innate T cells

Innate T cells (ITCs), including invariant natural killer T (iNKT) cells, mucosal-associated invariant T (MAIT) cells, and {gamma}{delta} T cell populations, use conserved antigen receptors generated by somatic recombination to respond to non-peptide antigens in an innate-like manner. Understanding where these cells fit in the scheme of immunity has been a puzzle since their discovery. Here, immunophenotyping of 101 individuals revealed that these populations account for as much as 25% of peripheral human T cells. To better understand these cells, we generated detailed gene expression profiles using low-input RNA-seq and confirmed key findings through protein-level and functional validation. Unbiased transcriptomic analyses revealed a continuous innateness gradient with adaptive T cells at one end followed by MAIT, iNKT, V{delta}1+ T, V{delta}2+ T, and natural killer cells at the other end. Innateness was characterized by decreased expression of translational machinery genes and reduced proliferative potential, which allowed for prioritization of effector functions, including rapid cytokine and chemokine production, and cytotoxicity. Thus, global transcriptional programs uncovered rapid proliferation and rapid effector functions as competing goals that respectively define adaptive and innate states.\n\nOne Sentence SummaryAdaptive and innate T cells align along a continuous innateness gradient, reflecting a trade-off between effector function and proliferative capacity.

immunology↗