bioRxiv : the preprint server for biology最新文献

Automated detection of complex animal behavior remains a challenge in neuroscience. Developments in computer vision have greatly advanced automated behavior detection and allow high-throughput preclinical and mechanistic studies. An integrated hardware and software solution is necessary to facilitate the adoption of these advances in the field of behavioral neurogenetics, particularly for non-computational laboratories. We have published a series of papers using an open field arena to annotate complex behaviors such as grooming, posture, and gait as well as higher-level constructs such as biological age and pain. Here, we present our, integrated rodent phenotyping platform, JAX Animal Behavior System (JABS), to the community for data acquisition, machine learning-based behavior annotation and classification, classifier sharing, and genetic analysis. The JABS Data Acquisition Module (JABS-DA) enables uniform data collection with its combination of 3D hardware designs and software for real-time monitoring and video data collection. JABS-Active Learning Module (JABS-AL) allows behavior annotation, classifier training, and validation. We introduce a novel graph-based framework (ethograph) that enables efficient boutwise comparison of JABS-AL classifiers. JABS-Analysis and Integration Module (JABS-AI), a web application, facilitates users to deploy and share any classifier that has been trained on JABS, reducing the effort required for behavior annotation. It supports the inference and sharing of the trained JABS classifiers and downstream genetic analyses (heritability and genetic correlation) on three curated datasets spanning 168 mouse strains that we are publicly releasing alongside this study. This enables the use of genetics as a guide to proper behavior classifier selection. This open-source tool is an ecosystem that allows the neuroscience and genetics community for shared advanced behavior analysis and reduces the barrier to entry into this new field.

Crossover recombination supports meiotic chromosome inheritance and fertility by establishing chiasmata between homologous chromosomes prior to the first meiotic division. In addition to the physical exchange of DNA mediated by meiotic recombination, chiasma formation also involves restructuring of the underlying chromosome axis, possibly to help with chiasma maturation or to resolve chromosomal interlocks. Here, we identify condensin as an important regulator of axis remodeling in S. cerevisiae. Condensin is recruited near sites of meiotic crossover designation by pro-crossover factors but is largely dispensable for DNA exchange. Instead, condensin helps to create discontinuities in the meiotic chromosome axis by promoting removal of cohesin. In addition, chromosomes of condensin mutants exhibit unusually common parallel chromatin clouds and experience a chromosomal buildup of the conserved axis remodeler Pch2. Consistent with an important role of axis restructuring at crossover sites, the canonical anaphase-bridge phenotype of condensin mutants is partly rescued by redirecting meiotic DNA repair to sister chromatids instead of homologous chromosomes, suggesting that crossover-associated axis reorganization is important for faithful meiotic chromosome segregation.

Ribosome profiling is a valuable methodology for measuring changes in a cell's translational program. The approach can report how efficiently mRNA coding sequences are translated and pinpoint positions along mRNAs where ribosomes slow down or arrest. It can also reveal when translation takes place outside coding regions, often with important regulatory consequences. While many useful software tools have emerged to facilitate analysis of these data, packages can become complex and challenging to adapt to specialized needs. We therefore introduce ribofootPrinter, a suite of Python tools designed to offer an accessible and modifiable set of code for analysis of data from ribosome profiling and related types of small RNA sequencing experiments. Alignments are made to a simplified transcriptome to keep the code intuitive and multiple normalization options help facilitate interpretation of meta analysis, particularly outside coding regions. We demonstrate how mapping of short reads to the transcriptome increases the frequency of matches to multiple sites and we provide multimapper identifier files to highlight these regions. Overall, this tool has the capability to carry out sophisticated analysis while maintaining enough simplicity to make it readily understandable and adaptable.

Most genome-wide association signals for complex disease reside in the noncoding genome, where defining function is nontrivial. MPRAs (massively parallel reporter assays) offer a scalable means to identify functional regulatory elements, but are typically conducted without regard to cell type, pairing cloned fragments with a generic housekeeping promoter. To explore the context-sensitivity of MPRAs, we screened enhancer activity across a panel of nearly 12,000 198-bp fragments spanning over 300 type 2 diabetes- and metabolic trait-associated regions in the 832/13 rat insulinoma beta cell line, a relevant model of pancreatic beta cells. We explored these fragments' context sensitivity by comparing their activities when placed up- or downstream of a reporter gene, and in combination with either a synthetic housekeeping promoter (SCP1) or a more biologically relevant promoter corresponding to the human insulin ( INS ) gene. We identified clear effects of MPRA construct design on enhancer activity. Specifically, a subset of fragments (n = 702/11,656) displayed positional bias, evenly distributed across up- and downstream preference. Promoter choice also influenced MPRA activity (n = 698/11,656), mostly biased towards the cell-specific INS promoter (73.4%). To identify sequence features associated with promoter preference, we used Lasso regression with 562 genomic annotations and discovered that fragments with INS promoter-biased activity are enriched for HNF1 motifs. HNF1 family transcription factors are key regulators of glucose metabolism disrupted in maturity onset diabetes of the young (MODY), suggesting genetic convergence between rare coding variants that cause MODY and common T2D-associated regulatory regions. We designed a follow-up MPRA containing HNF1 motif-enriched fragments and observed several instances where deletion or mutation of HNF1 motifs disrupted the INS promoter-biased enhancer activity, specifically in the beta cell model but not in a skeletal muscle cell line, another diabetes-relevant cell type. Together, our study suggests that cell-specific regulatory activity is partially influenced by enhancer-promoter compatibility and indicates that careful attention should be paid when designing MPRA libraries to capture context-specific regulatory processes at disease-associated genetic signals.

KRAS ^G12C inhibitors (G12Ci) have produced encouraging, albeit modest and transient, clinical benefit in pancreatic ductal adenocarcinoma (PDAC). Identifying and targeting resistance mechanisms to G12Ci treatment is therefore crucial. To better understand the function of KRAS ^G12C and possible G12Ci bypass mechanisms, we developed an autochthonous KRAS ^G12C - driven PDAC model. Compared to the classical KRAS ^G12D PDAC model, the G12C model exhibits slower tumor growth, yet similar histopathological and molecular features. Aligned with clinical experience, G12Ci treatment of KRAS ^G12C tumors produced modest impact despite stimulating a 'hot' tumor immune microenvironment. Immunoprofiling revealed that CD24, a 'don't eat me' signal, is significantly upregulated on cancer cells upon G12Ci treatment. Blocking CD24 enhanced macrophage phagocytosis of cancer cells and significantly sensitized tumors to G12Ci treatment. Similar findings were observed in KRAS ^G12D -driven PDAC. Together, this study reveals common and distinct oncogenic KRAS allele-specific biology and identifies a clinically actionable adaptive mechanism that may improve the efficacy of oncogenic KRAS inhibitor therapy in PDAC.

Significance: Generation of an autochthonous KRAS ^G12C -driven pancreatic cancer model enabled elucidation of specific effects of KRAS ^G12C during tumor development, revealing CD24 as an actionable adaptive mechanism in cancer cells induced upon KRAS ^G12C inhibition.

Touch plays a key role in our perception of our body and shapes our interactions with the world, from the objects we manipulate to the people we touch. While the tactile sensibility of the hand has been extensively characterized, much less is known about touch on other parts of the body. Despite the important role of the breast in lactation as well as in affective and sexual touch, relatively little is known about its sensory properties. To fill this gap, we investigated the spatial acuity of the breast and compared it to that of the hand and back, body regions that span the range of tactile spatial acuity. First, we found that the tactile acuity of the breast was even lower than that of the back, heretofore the paragon of poor acuity. Second, acuity was lower for larger breasts, consistent with the hypothesis that innervation capacity does not scale with body size. Third, touches to different regions of the nipple were largely indistinguishable, suggesting that the nipple is a sensory unit. Fourth, localization errors were systematically biased toward the nipple.

Large language models (LLMs) show promise in biomedical research, but their effectiveness for genomic inquiry remains unclear. We developed GeneTuring, a benchmark consisting of 16 genomics tasks with 1,600 curated questions, and manually evaluated 48,000 answers from ten LLM configurations, including GPT-4o (via API, ChatGPT with web access, and a custom GPT setup), GPT-3.5, Claude 3.5, Gemini Advanced, GeneGPT (both slim and full), BioGPT, and BioMedLM. A custom GPT-4o configuration integrated with NCBI APIs, developed in this study as SeqSnap, achieved the best overall performance. GPT-4o with web access and GeneGPT demonstrated complementary strengths. Our findings highlight both the promise and current limitations of LLMs in genomics, and emphasize the value of combining LLMs with domain-specific tools for robust genomic intelligence. GeneTuring offers a key resource for benchmarking and improving LLMs in biomedical research.

Primary motor cortex (M1) plays a central role in voluntary movement, but how it integrates sensory-driven corrective instructions is unclear. We analyzed population activity recorded from M1 of macaques during a sequential arm movement task with target updates requiring online adjustments to the motor plan. Using Latent Factor Analysis via Dynamical Systems (LFADS), we separated neural activity into two components: intrinsic dynamics and inferred external inputs influencing those dynamics. Inferred input timing was more strongly locked to target appearance than to movement onset, suggesting that variable reaction times reflect interactions between inputs and ongoing dynamics. Inferred inputs were tuned similarly for both initial and corrective movements, suggesting a shared input encoding across visually-instructed and corrective movements that was previously obscured by M1 dynamics. Because input inference can suffer from the challenge of nonidentifiability, where different models fit the data indistinguishably, we used ensembles of models with varied hyperparameters to diagnose when inputs are identifiable or nonidentifiable. In the monkey data, ensembles produced consistently similar results, suggesting that inputs could be meaningfully inferred and that their encoding was not simply a result of model bias. These results highlight the challenges of nonidentifiability and the potential of model ensembles to identify inputs in ongoing dynamics, at least in some cases.

Recent studies have shown that neural representation and processing are widely distributed in the brains of behaving animals [1, 2, 3, 4]. These observations challenge functional specialization as a central tenet of Neuroscience, which refers to the notion that distinct brain regions are dedicated to specific aspects of cognition such as working memory or subjective decision-making. Here we develop the concept of bifurcation in space to mechanistically account for the emergence of functional specialization that is compatible with distributed neural coding in a large-scale neocortex. Our theory starts with a departure from the canonical local circuit principle [5] by highlighting differences between cortical areas in the form of experimentally quantified heterogeneities of synaptic excitation and inhibition. We investigated connectome-based modelling of a multiregional cortex for both macaque monkeys and mice, as well as a generative model of a spatially embedded neocortex. During working memory in a simulated delayed response task, surprisingly, we found an inverted-V-shaped pattern of neuronal timescales across the cortical hierarchy as a signature of functional modularity, in sharp contrast to an increasing pattern of timescales during the resting state, as reported previously [6]. Furthermore, our model cortex simultaneously and robustly displays a plethora of bifurcations in space and their associated rich repertoire of timescale profiles across a large-scale cortex; the corresponding functionally defined modules (spatial attractors) could potentially subserve various internal mental processes. This work yields several specific experimentally testable predictions, including an inverted-V pattern of timescales, a measure of comparison between functional modules and structural modules defined by the graph theory, and a new plot for revealing bifurcation in space in neural activity recorded from animals performing different tasks that engage various functional modules. We propose that bifurcation in space, resulting from the connectome and macroscopic gradients of neurobiological properties across the cortex, represents a fundamental principle for understanding the brain's functional specialization and modular organization.

Recent work has demonstrated that both permanent lesions and acute inactivation experiments can lead to erroneous conclusions about the causal role of brain areas in specific behaviors, casting serious doubt on major avenues by which neuroscientists study the brain. To overcome this challenge, we developed a three-stage optogenetic approach which leverages the ability to precisely control the temporal period of regional inactivation with either brief or sustained illumination, enabling investigators to dissociate between putative 'permissive' and 'instructive' roles of brain areas in behavior. We applied this approach to the mouse primary visual cortex (V1) to probe whether V1 is permissive or instructive for the detection low contrast stimuli. Acute inactivation of V1 drastically suppressed performance, but during persistent inactivation, the animals' contrast detection recovered to pre-silencing levels. This recovery was itself reversible, as returning the animals to intermittent V1 inactivation reinstated the behavioral deficit. These results argue that V1 is the default circuit mice use to detect visual stimuli, but in its absence, other regions can compensate for it. This novel, temporally controllable optogenetic perturbation paradigm should be useful in other brain circuits to assess whether they are instructive or permissive in a brain function or behavior.