Spatiotemporal Adaptations of Macrophage and Dendritic Cell Development and Function.

Macrophages and conventional dendritic cells (cDCs) are distributed throughout the body, maintaining tissue homeostasis and tolerance to self and orchestrating innate and adaptive immunity against infection and cancer. As they complement each other, it is important to understand how they cooperate and the mechanisms that integrate their functions. Both are exposed to commensal microbes, pathogens, and other environmental challenges that differ widely among anatomical locations and over time. To adjust to these varying conditions, macrophages and cDCs acquire spatiotemporal adaptations (STAs) at different stages of their life cycle that determine how they respond to infection. The STAs acquired in response to previous infections can result in increased responsiveness to infection, termed training, or in reduced responses, termed paralysis, which in extreme cases can cause immunosuppression. Understanding the developmental stage and location where macrophages and cDCs acquire their STAs, and the molecular and cellular players involved in their induction, may afford opportunities to harness their beneficial outcomes and avoid or reverse their deleterious effects. Here we review our current understanding of macrophage and cDC development, life cycle, function, and STA acquisition before, during, and after infection.We propose a unified framework to explain how these two cell types adjust their activities to changing conditions over space and time to coordinate their immunosurveillance functions.

The molecular athlete: exercise physiology from mechanisms to medals.

Human skeletal muscle demonstrates remarkable plasticity, adapting to numerous external stimuli including the habitual level of contractile loading. Accordingly, muscle function and exercise capacity encompass a broad spectrum, from inactive individuals with low levels of endurance and strength to elite athletes who produce prodigious performances underpinned by pleiotropic training-induced muscular adaptations. Our current understanding of the signal integration, interpretation, and output coordination of the cellular and molecular mechanisms that govern muscle plasticity across this continuum is incomplete. As such, training methods and their application to elite athletes largely rely on a "trial-and-error" approach, with the experience and practices of successful coaches and athletes often providing the bases for "post hoc" scientific enquiry and research. This review provides a synopsis of the morphological and functional changes along with the molecular mechanisms underlying exercise adaptation to endurance- and resistance-based training. These traits are placed in the context of innate genetic and interindividual differences in exercise capacity and performance, with special consideration given to aging athletes. Collectively, we provide a comprehensive overview of skeletal muscle plasticity in response to different modes of exercise and how such adaptations translate from "molecules to medals."

Mechanisms of mechanical overload-induced skeletal muscle hypertrophy: current understanding and future directions.

Mechanisms underlying mechanical overload-induced skeletal muscle hypertrophy have been extensively researched since the landmark report by Morpurgo (1897) of "work-induced hypertrophy" in dogs that were treadmill trained. Much of the preclinical rodent and human resistance training research to date supports that involved mechanisms include enhanced mammalian/mechanistic target of rapamycin complex 1 (mTORC1) signaling, an expansion in translational capacity through ribosome biogenesis, increased satellite cell abundance and myonuclear accretion, and postexercise elevations in muscle protein synthesis rates. However, several lines of past and emerging evidence suggest that additional mechanisms that feed into or are independent of these processes are also involved. This review first provides a historical account of how mechanistic research into skeletal muscle hypertrophy has progressed. A comprehensive list of mechanisms associated with skeletal muscle hypertrophy is then outlined, and areas of disagreement involving these mechanisms are presented. Finally, future research directions involving many of the discussed mechanisms are proposed.

Guidelines for organizing accessible and inclusive research retreats.

Research retreats are elements of scientific graduate training programs. Although expected to provide strong educational value, some students are reluctant to attend. Here, we identify participation barriers and provide guidelines for retreat design that minimize obstacles and establish an inclusive environment to improve attendance and enrichment for all attendees.

Interferons and epigenetic mechanisms in training, priming and tolerance of monocytes and hematopoietic progenitors.

Training and priming of innate immune cells involve preconditioning by PAMPs, DAMPs, and/or cytokines that elicits stronger induction of inflammatory genes upon secondary challenge. Previous models distinguish training and priming based upon whether immune activation returns to baseline prior to secondary challenge. Tolerance is a protective mechanism whereby potent stimuli induce refractoriness to secondary challenge. Training and priming are important for innate memory responses that protect against infection, efficacy of vaccines, and maintaining innate immune cells in a state of readiness; tolerance prevents toxicity from excessive immune activation. Dysregulation of these processes can contribute to pathogenesis of autoimmune/inflammatory conditions, post-COVID-19 hyperinflammatory states, or sepsis-associated immunoparalysis. Training, priming, and tolerance regulate similar "signature" inflammatory genes such as TNF, IL6, and IL1B and utilize overlapping epigenetic mechanisms. We review how interferons (IFNs), best known for activating JAK-STAT signaling and interferon-stimulated genes, also play a key role in regulating training, priming, and tolerance via chromatin-mediated mechanisms. We present new data on how monocyte-to-macrophage differentiation modulates IFN-γ-mediated priming, affects regulation of AP-1 and CEBP activity, and attenuates superinduction of inflammatory genes. We present a "training-priming continuum" model that integrates IFN-mediated priming into current concepts about training and tolerance and proposes a central role for STAT1 and IRF1.

Discordant skeletal muscle gene and protein responses to exercise.

The ability of skeletal muscle to adapt to repeated contractile stimuli is one of the most intriguing aspects of physiology. The molecular bases underpinning these adaptations involve increased protein activity and/or expression, mediated by an array of pre- and post-transcriptional processes, as well as translational and post-translational control. A longstanding dogma assumes a direct relationship between exercise-induced increases in mRNA levels and subsequent changes in the abundance of the proteins they encode. Drawing on the results of recent studies, we dissect and question the common assumption of a direct relationship between changes in the skeletal muscle transcriptome and proteome induced by repeated muscle contractions (e.g., exercise).

A multi-epigenomic map of endurance exercise training.

The Molecular Transducers of Physical Activity Consortium (MoTrPAC) aims to comprehensively map molecular alterations in response to acute exercise and chronic training. In one of a recent series of papers from MoTrPAC, Nair et al. provide the first multi-epigenomic and transcriptomic integration across eight tissues in both sexes following adaptation to endurance exercise training (EET).

The consequences of AI training on human decision-making.

AI is now an integral part of everyday decision-making, assisting us in both routine and high-stakes choices. These AI models often learn from human behavior, assuming this training data is unbiased. However, we report five studies that show that people change their behavior to instill desired routines into AI, indicating this assumption is invalid. To show this behavioral shift, we recruited participants to play the ultimatum game, where they were asked to decide whether to accept proposals of monetary splits made by either other human participants or AI. Some participants were informed their choices would be used to train an AI proposer, while others did not receive this information. Across five experiments, we found that people modified their behavior to train AI to make fair proposals, regardless of whether they could directly benefit from the AI training. After completing this task once, participants were invited to complete this task again but were told their responses would not be used for AI training. People who had previously trained AI persisted with this behavioral shift, indicating that the new behavioral routine had become habitual. This work demonstrates that using human behavior as training data has more consequences than previously thought since it can engender AI to perpetuate human biases and cause people to form habits that deviate from how they would normally act. Therefore, this work underscores a problem for AI algorithms that aim to learn unbiased representations of human preferences.

Broadening access to cryoEM through centralized facilities.

Cryogenic electron microscopy (cryoEM) uses images of frozen hydrated biological specimens to produce macromolecular structures, opening up previously inaccessible levels of biological organization to high-resolution structural analysis. CryoEM has the potential for broad impact in biomedical research, including basic cell, molecular, and structural biology, and increasingly in drug discovery and vaccine development. Recent advances have led to the expansion of molecular and cellular structure determination at an exponential rate. National and regional centers have emerged to support this growth by increasing the accessibility of cryoEM throughout the biomedical research community. Through cooperation and synergy, these centers form a network of resources that accelerate the adoption of best practices for access and training and establish sustainable workflows to build future research capacity.

Exercise training and changes in skeletal muscle mitochondrial proteins: from blots to "omics".

Mitochondria are essential, membrane-enclosed organelles that consist of ∼1100 different proteins, which allow for many diverse functions critical to maintaining metabolism. Highly metabolic tissues, such as skeletal muscle, have a high mitochondrial content that increases with exercise training. The classic western blot technique has revealed training-induced increases in the relatively small number of individual mitochondrial proteins studied (∼5% of the >1100 proteins in MitoCarta), with some of these changes dependent on the training stimulus. Proteomic approaches have identified hundreds of additional mitochondrial proteins that respond to exercise training. There is, however, surprisingly little crossover in the mitochondrial proteins identified in the published human training studies. This suggests that to better understand the link between training-induced changes in mitochondrial proteins and metabolism, future studies need to move beyond maximizing protein detection to adopting methods that will increase the reliability of the changes in protein abundance observed.

c-Maf enforces cytokine production and promotes memory-like responses in mouse and human type 2 innate lymphoid cells.

Group-2 innate lymphoid cells (ILC2s), which are involved in type 2 inflammatory diseases such as allergy, can exhibit immunological memory, but the basis of this ILC2 "trained immunity" has remained unclear. Here, we found that stimulation with IL-33/IL-25 or exposure to the allergen papain induces the expression of the transcription factor c-Maf in mouse ILC2s. Chronic papain exposure results in high production of IL-5 and IL-13 cytokines and lung eosinophil recruitment, effects that are blocked by c-Maf deletion in ILCs. Transcriptomic analysis revealed that knockdown of c-Maf in ILC2s suppresses expression of type 2 cytokine genes, as well as of genes linked to a memory-like phenotype. Consistently, c-Maf was found highly expressed in human adult ILC2s but absent in cord blood and required for cytokine production in isolated human ILC2s. Furthermore, c-Maf-deficient mouse or human ILC2s failed to exhibit strengthened ("trained") responses upon repeated challenge. Thus, the expression of c-Maf is indispensable for optimal type 2 cytokine production and proper memory-like responses in group-2 innate lymphoid cells.

Cloud-based introduction to BASH programming for biologists.

This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning', https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial authored by National Institute of General Medical Sciences: NIGMS Sandbox: A Learning Platform toward Democratizing Cloud Computing for Biomedical Research at the beginning of this supplement. This module delivers learning materials introducing the utility of the BASH (Bourne Again Shell) programming language for genomic data analysis in an interactive format that uses appropriate cloud resources for data access and analyses. The next-generation sequencing revolution has generated massive amounts of novel biological data from a multitude of platforms that survey an ever-growing list of genomic modalities. These data require significant downstream computational and statistical analyses to glean meaningful biological insights. However, the skill sets required to generate these data are vastly different from the skills required to analyze these data. Bench scientists that generate next-generation data often lack the training required to perform analysis of these datasets and require support from bioinformatics specialists. Dedicated computational training is required to empower biologists in the area of genomic data analysis, however, learning to efficiently leverage a command line interface is a significant barrier in learning how to leverage common analytical tools. Cloud platforms have the potential to democratize access to the technical tools and computational resources necessary to work with modern sequencing data, providing an effective framework for bioinformatics education. This module aims to provide an interactive platform that slowly builds technical skills and knowledge needed to interact with genomics data on the command line in the Cloud. The sandbox format of this module enables users to move through the material at their own pace and test their grasp of the material with knowledge self-checks before building on that material in the next sub-module. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

A cloud-based training module for efficient de novo transcriptome assembly using Nextflow and Google cloud.

This study describes the development of a resource module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" (https://github.com/NIGMS/NIGMS-Sandbox). The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on de novo transcriptome assembly using Nextflow in an interactive format that uses appropriate cloud resources for data access and analysis. Cloud computing is a powerful new means by which biomedical researchers can access resources and capacity that were previously either unattainable or prohibitively expensive. To take advantage of these resources, however, the biomedical research community needs new skills and knowledge. We present here a cloud-based training module, developed in conjunction with Google Cloud, Deloitte Consulting, and the NIH STRIDES Program, that uses the biological problem of de novo transcriptome assembly to demonstrate and teach the concepts of computational workflows (using Nextflow) and cost- and resource-efficient use of Cloud services (using Google Cloud Platform). Our work highlights the reduced necessity of on-site computing resources and the accessibility of cloud-based infrastructure for bioinformatics applications.

Reusable tutorials for using cloud-based computing environments for the analysis of bacterial gene expression data from bulk RNA sequencing.

This manuscript describes the development of a resource module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on RNA sequencing (RNAseq) data analysis in an interactive format that uses appropriate cloud resources for data access and analyses. Biomedical research is increasingly data-driven, and dependent upon data management and analysis methods that facilitate rigorous, robust, and reproducible research. Cloud-based computing resources provide opportunities to broaden the application of bioinformatics and data science in research. Two obstacles for researchers, particularly those at small institutions, are: (i) access to bioinformatics analysis environments tailored to their research; and (ii) training in how to use Cloud-based computing resources. We developed five reusable tutorials for bulk RNAseq data analysis to address these obstacles. Using Jupyter notebooks run on the Google Cloud Platform, the tutorials guide the user through a workflow featuring an RNAseq dataset from a study of prophage altered drug resistance in Mycobacterium chelonae. The first tutorial uses a subset of the data so users can learn analysis steps rapidly, and the second uses the entire dataset. Next, a tutorial demonstrates how to analyze the read count data to generate lists of differentially expressed genes using R/DESeq2. Additional tutorials generate read counts using the Snakemake workflow manager and Nextflow with Google Batch. All tutorials are open-source and can be used as templates for other analysis.

Heterogeneous biomedical entity representation learning for gene-disease association prediction.

Understanding the genetic basis of disease is a fundamental aspect of medical research, as genes are the classic units of heredity and play a crucial role in biological function. Identifying associations between genes and diseases is critical for diagnosis, prevention, prognosis, and drug development. Genes that encode proteins with similar sequences are often implicated in related diseases, as proteins causing identical or similar diseases tend to show limited variation in their sequences. Predicting gene-disease association (GDA) requires time-consuming and expensive experiments on a large number of potential candidate genes. Although methods have been proposed to predict associations between genes and diseases using traditional machine learning algorithms and graph neural networks, these approaches struggle to capture the deep semantic information within the genes and diseases and are dependent on training data. To alleviate this issue, we propose a novel GDA prediction model named FusionGDA, which utilizes a pre-training phase with a fusion module to enrich the gene and disease semantic representations encoded by pre-trained language models. Multi-modal representations are generated by the fusion module, which includes rich semantic information about two heterogeneous biomedical entities: protein sequences and disease descriptions. Subsequently, the pooling aggregation strategy is adopted to compress the dimensions of the multi-modal representation. In addition, FusionGDA employs a pre-training phase leveraging a contrastive learning loss to extract potential gene and disease features by training on a large public GDA dataset. To rigorously evaluate the effectiveness of the FusionGDA model, we conduct comprehensive experiments on five datasets and compare our proposed model with five competitive baseline models on the DisGeNet-Eval dataset. Notably, our case study further demonstrates the ability of FusionGDA to discover hidden associations effectively. The complete code and datasets of our experiments are available at https://github.com/ZhaohanM/FusionGDA.

A global initiative on addressing bioinformatics' grand challenges.

The Bioinformatics Grand Challenges Consortium (BGCC) is a collaborative effort to address the most pressing challenges in bioinformatics. Initially focusing on education and training, the consortium successfully defined seven key grand challenges and is actively developing actionable solutions for these challenges. Building on this foundation, the BGCC plans to broaden its focus to include additional grand challenges in emerging areas.

Exercise Inhibits Doxorubicin-Induced Cardiotoxicity via Regulating B Cells.

BACKGROUND: Doxorubicin is an effective chemotherapeutic agent, but its use is limited by acute and chronic cardiotoxicity. Exercise training has been shown to protect against doxorubicin-induced cardiotoxicity, but the involvement of immune cells remains unclear. This study aimed to investigate the role of exercise-derived B cells in protecting against doxorubicin-induced cardiotoxicity and to further determine whether B cell activation and antibody secretion play a role in this protection. METHODS: Mice that were administered with doxorubicin (5 mg/kg per week, 20 mg/kg cumulative dose) received treadmill running exercise. The adoptive transfer of exercise-derived splenic B cells to µMT-/- (B cell-deficient) mice was performed to elucidate the mechanism of B cell regulation that mediated the effect of exercise. RESULTS: Doxorubicin-administered mice that had undergone exercise training showed improved cardiac function, and low levels of cardiac apoptosis, atrophy, and fibrosis, and had reduced cardiac antibody deposition and proinflammatory responses. Similarly, B cell pharmacological and genetic depletion alleviated doxorubicin-induced cardiotoxicity, which phenocopied the protection of exercise. In vitro performed coculture experiments confirmed that exercise-derived B cells reduced cardiomyocyte apoptosis and fibroblast activation compared with control B cells. Importantly, the protective effect of exercise on B cells was confirmed by the adoptive transfer of splenic B cells from exercised donor mice to µMT-/- recipient mice. However, blockage of Fc gamma receptor IIB function using B cell transplants from exercised Fc gamma receptor IIB-/- mice abolished the protection of exercise-derived B cells against doxorubicin-induced cardiotoxicity. Mechanistically, we found that Fc gamma receptor IIB, an important B cell inhibitory receptor, responded to exercise and increased B cell activation threshold, which participated in exercise-induced protection against doxorubicin-induced cardiotoxicity. CONCLUSIONS: Our results demonstrate that exercise training protects against doxorubicin-induced cardiotoxicity by upregulating Fc gamma receptor IIB expression in B cells, which plays an important anti-inflammatory role and participates in the protective effect of exercise against doxorubicin-induced cardiotoxicity.

Proteomic Profiling of Muscular Adaptations to Short-Term Concentric Versus Eccentric Exercise Training in Humans.

The molecular mechanisms underlying muscular adaptations to concentric (CON) and eccentric (ECC) exercise training have been extensively explored. However, most previous studies have focused on specifically selected proteins, thus, unable to provide a comprehensive protein profile and potentially missing the crucial mechanisms underlying muscular adaptation to exercise training. We herein aimed to investigate proteomic profiles of human skeletal muscle in response to short-term resistance training. Twenty young males were randomly and evenly assigned to two groups to complete a 4-week either ECC or CON training program. Measurements of body composition and physiological function of the quadriceps femoris were conducted both before and after the training. Muscle biopsies from the vastus lateralis of randomly selected participants (five in ECC and four in CON) of both before and after the training were analyzed using the liquid-chromatography tandem mass spectrometry in combination with bioinformatics analysis. Neither group presented a significant difference in body composition or leg muscle mass; however, muscle peak torque, total work, and maximal voluntary contraction were significantly increased after the training in both groups. Proteomics analysis revealed 122 differentially abundant proteins (DAPs; p value < 0.05 & fold change >1.5 or <0.67) in ECC, of which the increased DAPs were mainly related to skeletal muscle contraction and cytoskeleton and enriched specifically in the pentose phosphate pathway, extracellular matrix-receptor interaction, and PI3K-Akt signaling pathway, whereas the decreased DAPs were associated with the mitochondrial respiratory chain. One hundred one DAPs were identified in CON, of which the increased DAPs were primarily involved in translation/protein synthesis and the mitochondria respiratory, whereas the decreased DAPs were related to metabolic processes, cytoskeleton, and de-ubiquitination. In conclusion, the 4-week CON and ECC training resulted in distinctly different proteomic profiles, especially in proteins related to muscular structure and metabolism.

Learning to learn by using nonequilibrium training protocols for adaptable materials.

Evolution in time-varying environments naturally leads to adaptable biological systems that can easily switch functionalities. Advances in the synthesis of environmentally responsive materials therefore open up the possibility of creating a wide range of synthetic materials which can also be trained for adaptability. We consider high-dimensional inverse problems for materials where any particular functionality can be realized by numerous equivalent choices of design parameters. By periodically switching targets in a given design algorithm, we can teach a material to perform incompatible functionalities with minimal changes in design parameters. We exhibit this learning strategy for adaptability in two simulated settings: elastic networks that are designed to switch deformation modes with minimal bond changes and heteropolymers whose folding pathway selections are controlled by a minimal set of monomer affinities. The resulting designs can reveal physical principles, such as nucleation-controlled folding, that enable such adaptability.

Faculty as catalysts for training new inventors: Differential outcomes for male and female PhD students.

STEM PhDs are a critical source of human capital in the economy, contributing to commercial as well as academic science. We examine whether STEM PhD students become new inventors (file their first patent) during their doctoral training at the top 25 U.S. universities (by patenting). We find that 4% of PhDs become new inventors. However, among PhDs of faculty who are themselves top (prolific) inventors, this figure rises to 23%. These faculty train 44% of all the new inventor PhDs by copatenting with their advisees. We also explore whether new inventor PhDs are equally distributed by gender. In our university sample, the female share of new inventors is 9% points (pp) lower than the female share of PhDs. Several channels contribute to this: First, female PhDs are less likely to be trained by top inventor advisors (TIs) than male PhDs. Second, they are less likely to be trained by (the larger number of) male top inventors: The estimated gap in the female % of PhDs between female and male TIs is 7 to 9 pp. Third, female PhDs (supervised by top inventors and especially by other faculty) have a lower probability of becoming new inventors relative to their male counterparts. Notably, we find that male and female top inventors have similar rates of transforming their female advisees into new inventors at 4 to 8 pp lower (17 to 26% lower rate) than for male advisees. The gap remains at 4 pp comparing students of the same advisor and controlling for thesis topic.