bioRxiv : the preprint server for biology最新文献

Endo-lysosomes are considered acidic Ca ²⁺ stores but direct measurements of luminal Ca ²⁺ within them are limited. Here we report that the Ca ²⁺ -sensitive luminescent protein aequorin does not reconstitute with its cofactor at highly acidic pH but that a significant fraction of the probe is functional within a mildly acidic compartment when targeted to the endo-lysosomal system. We leveraged this probe (ELGA) to report Ca ²⁺ dynamics in this compartment. We show that Ca ²⁺ uptake is ATP-dependent and sensitive to blockers of endoplasmic reticulum Ca ²⁺ pumps. We find that the Ca ²⁺ mobilizing messenger IP ₃ which typically targets the endoplasmic reticulum evokes robust luminal responses in wild type cells, but not in IP ₃ receptor knock-out cells. Responses were comparable to those evoked by activation of the endo-lysosomal ion channel TRPML1. Stimulation with IP ₃ -forming agonists also mobilized the store in intact cells. Super-resolution microscopy analysis confirmed the presence of IP ₃ receptors within the endo-lysosomal system, both in live and fixed cells. Our data reveal a physiologically-relevant, IP ₃ -sensitive store of Ca ²⁺ within the endo-lysosomal system.

A wide variety of human diseases are associated with loss of microbial diversity in the human gut, inspiring a great interest in the diagnostic or therapeutic potential of the microbiota. However, the ecological forces that drive diversity reduction in disease states remain unclear, rendering it difficult to ascertain the role of the microbiota in disease emergence or severity. One hypothesis to explain this phenomenon is that microbial diversity is diminished as disease states select for microbial populations that are more fit to survive environmental stress caused by inflammation or other host factors. Here, we tested this hypothesis on a large scale, by developing a software framework to quantify the enrichment of microbial metabolisms in complex metagenomes as a function of microbial diversity. We applied this framework to over 400 gut metagenomes from individuals who are healthy or diagnosed with inflammatory bowel disease (IBD). We found that high metabolic independence (HMI) is a distinguishing characteristic of microbial communities associated with individuals diagnosed with IBD. A classifier we trained using the normalized copy numbers of 33 HMI-associated metabolic modules not only distinguished states of health versus IBD, but also tracked the recovery of the gut microbiome following antibiotic treatment, suggesting that HMI is a hallmark of microbial communities in stressed gut environments.

A doctoral-level internship program was developed at the University of North Carolina at Chapel Hill with the intent to create customizable experiential learning opportunities for biomedical trainees to support career exploration, preparation, and transition into their post-graduate professional roles. We report the outcomes of this program over a five-year period. During that 5-year period, 123 internships took place at over 70 partner sites, representing at least 20 academic, for-profit, and non-profit career paths in the life sciences. A major goal of the program was to enhance trainees' skill development and expertise in careers of interest. The benefits of the internship program for interns, host/employer, and supervisor/principal investigator were assessed using a mixed-methods approach, including surveys with closed- and open-ended responses as well as focus group interviews. Balancing stakeholder interests is key to creating a sustainable program with widespread support; hence, the level of support from internship hosts and faculty members were key metrics analyzed throughout. We hypothesized that once a successful internship program was implemented, faculty culture might shift to be more accepting of internships; indeed, the data quantifying faculty attitudes support this. Furthermore, host motivation and performance expectations of interns were compared with results achieved, and this data revealed both expected and surprising benefits to hosts. Data suggests a myriad of benefits for each stakeholder group, and themes are cataloged and discussed. Program outcomes, evaluation data, policies, resources, and best practices developed through the implementation of this program are shared to provide resources that facilitate the creation of similar internship programs at other institutions. Program development was initially spurred by National Institutes of Health pilot funding, thereafter, successfully transitioning from a grant-supported model, to an institutionally supported funding model to achieve long-term programmatic sustainability.

Bone regeneration is mediated by skeletal stem/progenitor cells (SSPCs) that are mainly recruited from the periosteum after bone injury. The composition of the periosteum and the steps of SSPC activation and differentiation remain poorly understood. Here, we generated a single-nuclei atlas of the periosteum at steady-state and of the fracture site during early stages of bone repair ( https://fracture-repair-atlas.cells.ucsc.edu ). We identified periosteal SSPCs expressing stemness markers ( Pi16 and Ly6a /SCA1) and responding to fracture by adopting an injury-induced fibrogenic cell (IIFC) fate, prior to undergoing osteogenesis or chondrogenesis. We identified distinct gene cores associated with IIFCs and their engagement into osteogenesis and chondrogenesis involving Notch, Wnt and the circadian clock signaling respectively. Finally, we show that IIFCs are the main source of paracrine signals in the fracture environment, suggesting a crucial paracrine role of this transient IIFC population during fracture healing. Overall, our study provides a complete temporal topography of the early stages of fracture healing and the dynamic response of periosteal SSPCs to injury, redefining our knowledge of bone regeneration.

Recent work employed Pearson residuals from Poisson or negative binomial models to normalize UMI data. To extend this approach to non-UMI data, we model the additional amplification step with a compound distribution: we assume that sequenced RNA molecules follow a negative binomial distribution, and are then replicated following an amplification distribution. We show how this model leads to compound Pearson residuals, which yield meaningful gene selection and embeddings of Smart-seq2 datasets. Further, we suggest that amplification distributions across several sequencing protocols can be described by a broken power law. The resulting compound model captures previously unexplained overdispersion and zero-inflation patterns in non-UMI data.

Donor-specific antibody (DSA) responses against human leukocyte antigen (HLA) proteins mismatched between kidney transplant donors and recipients cause allograft loss. The rules governing the immunogenicity of non-self donor HLA are poorly understood. Using single-cell, molecular, structural, and proteomic techniques, we profiled the HLA-specific B cell response in the kidney and blood of a transplant recipient with antibody-mediated rejection (AMR). We observed an immunodominant B cell antibody response focused on topographically exposed, solvent-accessible mismatched HLA residues along the peptide-binding groove - a subregion comprising only 20% of the HLA molecule. We further demonstrated that, even within a diverse cohort of transplant recipients, the B cell alloresponse consistently converges on this same immunodominant subregion on the crown of the HLA molecule. Based on these findings, we propose that B cell immunodominance in transplant rejection relies on antigenic topography, and we suggest that this link could be exploited for organ matching and therapeutics.

The coronavirus disease of 2019 (COVID-19) pandemic is characterized by sequential emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, lineages, and sublineages, outcompeting previously circulating ones because of, among other factors, increased transmissibility and immune escape. We propose DeepAutoCoV, an unsupervised deep learning anomaly detection system to predict future dominant lineages (FDLs). We define FDLs as viral (sub)lineages that will constitute more than 10% of all the viral sequences added to the GISAID database on a given week. DeepAutoCoV is trained and validated by assembling global and country-specific data sets from over 16 million Spike protein sequences sampled over a period of about 4 years. DeepAutoCoV successfully flags FDLs at very low frequencies (0.01% - 3%), with median lead times of 4-17 weeks, and predicts FDLs ∼5 and ∼25 times better than a baseline approach For example, the B.1.617.2 vaccine reference strain was flagged as FDL when its frequency was only 0.01%, more than a year before it was considered for an updated COVID-19 vaccine. Furthermore, DeepAutoCoV outputs interpretable results by pinpointing specific mutations potentially linked to increased fitness, and may provide significant insights for the optimization of public health pre-emptive intervention strategies.

Neuromodulatory inputs to the hippocampus play pivotal roles in modulating synaptic plasticity, shaping neuronal activity, and influencing learning and memory. Recently it has been shown that the main sources of catecholamines to the hippocampus, ventral tegmental area (VTA) and locus coeruleus (LC), may have overlapping release of neurotransmitters and effects on the hippocampus. Therefore, to dissect the impacts of both VTA and LC circuits on hippocampal function, a thorough examination of how these pathways might differentially operate during behavior and learning is necessary. We therefore utilized 2-photon microscopy to functionally image the activity of VTA and LC axons within the CA1 region of the dorsal hippocampus in head-fixed male mice navigating linear paths within virtual reality (VR) environments. We found that within familiar environments some VTA axons and the vast majority of LC axons showed a correlation with the animals' running speed. However, as mice approached previously learned rewarded locations, a large majority of VTA axons exhibited a gradual ramping-up of activity, peaking at the reward location. In contrast, LC axons displayed a pre-movement signal predictive of the animal's transition from immobility to movement. Interestingly, a marked divergence emerged following a switch from the familiar to novel VR environments. Many LC axons showed large increases in activity that remained elevated for over a minute, while the previously observed VTA axon ramping-to-reward dynamics disappeared during the same period. In conclusion, these findings highlight distinct roles of VTA and LC catecholaminergic inputs in the dorsal CA1 hippocampal region. These inputs encode unique information, with reward information in VTA inputs and novelty and kinematic information in LC inputs, likely contributing to differential modulation of hippocampal activity during behavior and learning.

Foam cells are dysfunctional, lipid-laden macrophages associated with chronic inflammation of diverse origin. The long-standing paradigm that foam cells are cholesterol-laden derives from atherosclerosis research. We previously showed that, in tuberculosis, foam cells surprisingly accumulate triglycerides. Here, we utilized bacterial ( Mycobacterium tuberculosis ), fungal ( Cryptococcus neoformans ), and human papillary renal cell carcinoma (pRCC) models to address the need for a new explanation of foam cell biogenesis. We applied mass spectrometry-based imaging to assess the spatial distribution of storage lipids relative to foam-cell-rich areas in lesional tissues, and we characterized lipid-laden macrophages generated under corresponding in vitro conditions. The in vivo data and the in vitro findings showed that cryptococcus-infected macrophages accumulate triglycerides, while macrophages exposed to pRCC- conditioned-medium accumulated both triglycerides and cholesterol. Moreover, cryptococcus- and mycobacterium-infected macrophages accumulated triglycerides in different ways. Collectively, the data show that the molecular events underlying foam cell formation are specific to disease and microenvironment. Since foam cells are potential therapeutic targets, recognizing that their formation is disease-specific opens new biomedical research directions.

Movement flexibility and automaticity are necessary to successfully navigate different environments. When encountering difficult terrains such as a muddy trail, we can change how we step almost immediately so that we can continue walking. This flexibility comes at a cost since we initially must pay deliberate attention to how we are moving. Gradually, after a few minutes on the trail, stepping becomes automatic so that we do not need to think about our movements. Canonical theory indicates that different adaptive motor learning mechanisms confer these essential properties to movement: explicit control confers rapid flexibility, while forward model recalibration confers automaticity. Here we uncover a distinct mechanism of treadmill walking adaptation - an automatic stimulus-response mapping - that confers both properties to movement. The mechanism is flexible as it learns stepping patterns that can be rapidly changed to suit a range of treadmill configurations. It is also automatic as it can operate without deliberate control or explicit awareness by the participants. Our findings reveal a tandem architecture of forward model recalibration and automatic stimulus-response mapping mechanisms for walking, reconciling different findings of motor adaptation and perceptual realignment.