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Microbial physiological processes are intimately involved in nutrient cycling. However, it remains unclear to what extent microbial diversity or community composition is important for determining the rates of ecosystem-scale functions. There are many examples of positive correlations between microbial diversity and ecosystem function, but how microbial communities ‘map' onto ecosystem functions remain unresolved. This uncertainty limits our ability to predict and manage crucial microbially mediated processes such as nutrient losses and greenhouse gas emissions. To overcome this challenge, we propose integrating traditional biodiversity–ecosystem function research with ideas from genotype–phenotype mapping in organisms. We identify two insights from genotype–phenotype mapping that could be useful for microbial biodiversity–ecosystem function studies: the concept of searching ‘agnostically' for markers of ecosystem function and controlling for population stratification to identify microorganisms uniquely associated with ecosystem function. We illustrate the potential for these approaches to elucidate microbial biodiversity–ecosystem function relationships by analysing a subset of published data measuring methane oxidation rates from tropical soils. We assert that combining the approaches of traditional biodiversity–ecosystem function research with ideas from genotype–phenotype mapping will generate novel hypotheses about how complex microbial communities drive ecosystem function and help scientists predict and manage changes to ecosystem functions resulting from human activities. This article is part of the theme issue ‘Conceptual challenges in microbial community ecology’.

Mucociliary clearance (MCC) is one of the most important defence mechanisms of the human respiratory system. Its failure is implicated in many chronic and debilitating airway diseases. However, due to the complexity of lung organization, we currently lack full understanding on the relationship between these regional differences in anatomy and biology and MCC functioning. For example, it is unknown whether the regional variability of airway geometry, cell biology and ciliary mechanics play a functional role in MCC. It therefore remains unclear whether the regional preference seen in some airway diseases could originate from local MCC dysfunction. Though great insights have been gained into the genetic basis of cilia ultrastructural defects in airway ciliopathies, the scaling to regional MCC function and subsequent clinical phenotype remains unpredictable. Understanding the multiscale mechanics of MCC would help elucidate genotype–phenotype relationships and enable better diagnostic tools and treatment options. Here, we review the hierarchical and variable organization of ciliated airway epithelium in human lungs and discuss how this organization relates to MCC function. We then discuss the relevancy of these structure–function relationships to current topics in lung disease research. Finally, we examine how state-of-the-art computational approaches can help address existing open questions. This article is part of the Theo Murphy meeting issue ‘Unity and diversity of cilia in locomotion and transport’.

β-Glucans are a heterogeneous group of glucose polymers with a common structure comprising a main chain of β-(1,3) and/or β-(1,4)-glucopyranosyl units, along with side chains with various branches and lengths. β-Glucans initiate immune responses via immune cells, which become activated by the binding of the polymer to specific receptors. However, β-glucans from different sources also differ in their structure, conformation, physical properties, binding affinity to receptors, and thus biological functions. The mechanisms behind this are not fully understood. This mini-review provides a comprehensive and up-to-date commentary on the relationship between β-glucans' structure and function in relation to their use for immunomodulation.

Marine algae have attracted a great deal of interest as excellent sources of nutrients. Polysaccharides are the main components in marine algae, hence a great deal of attention has been directed at isolation and characterization of marine algae polysaccharides because of their numerous health benefits. In this review, extraction and purification approaches and chemico-physical properties of marine algae polysaccharides (MAPs) are summarized. The biological activities, which include immunomodulatory, antitumor, antiviral, antioxidant, and hypolipidemic, are also discussed. Additionally, structure-function relationships are analyzed and summarized. MAPs’ biological activities are closely correlated with their monosaccharide composition, molecular weights, linkage types, and chain conformation. In order to promote further exploitation and utilization of polysaccharides from marine algae for functional food and pharmaceutical areas, high efficiency, and low-cost polysaccharide extraction and purification methods, quality control, structure-function activity relationships, and specific mechanisms of MAPs activation need to be extensively investigated.

Network models describe the brain as sets of nodes and edges that represent its distributed organization. So far, most discoveries in network neuroscience have prioritized insights that highlight distinct groupings and specialized functional contributions of network nodes. Importantly, these functional contributions are determined and expressed by the web of their interrelationships, formed by network edges. Here, we underscore the important contributions made by brain network edges for understanding distributed brain organization. Different types of edges represent different types of relationships, including connectivity and similarity among nodes. Adopting a specific definition of edges can fundamentally alter how we analyze and interpret a brain network. Furthermore, edges can associate into collectives and higher order arrangements, describe time series, and form edge communities that provide insights into brain network topology complementary to the traditional node-centric perspective. Focusing on the edges, and the higher order or dynamic information they can provide, discloses previously underappreciated aspects of structural and functional network organization.

The structural characterization, the in vitro antioxidant activity, and the hypoglycemic activity of a polysaccharide (SGP-1-1) isolated from Siraitia grosvenorii (SG) were studied in this paper. SGP-1-1, whose molecular weight is 19.037 kDa, consisted of Gal:Man:Glc in the molar ratio of 1:2.56:4.90. According to the results of methylation analysis, GC–MS, and NMR, HSQC was interpreted as a glucomannan with a backbone composed of 4)-β-D-Glcp-(1→4)-, α-D-Glcp-(1→4)-, and 4)-Manp-(1 residues. α-1,6 linked an α-D-Galp branch, and α-1,6 linked an α-D-Glcp branch. The study indirectly showed that SGP-1-1 has good in vitro hypoglycemic and antioxidant activities and that these activities may be related to the fact that the SGP-1-1’s monosaccharide composition (a higher proportion of Gal and Man) is the glycosidic-bond type (α- and β-glycosidic bonds). SGP-1-1 could be used as a potential antioxidant and hypoglycemic candidate for functional and nutritional food applications.

Mice have been increasingly used as preclinical model to elucidate mechanisms and test therapeutics for treating intervertebral disc degeneration (IDD). Several intervertebral disc (IVD) histological scoring systems have been proposed, but none exists that reliably quantitate mouse disc pathologies. Here, we report a new robust quantitative mouse IVD histopathological scoring system developed by building consensus from the spine community analyses of previous scoring systems and features noted on different mouse models of IDD. The new scoring system analyzes 14 key histopathological features from nucleus pulposus (NP), annulus fibrosus (AF), endplate (EP), and AF/NP/EP interface regions. Each feature is categorized and scored; hence, the weight for quantifying the disc histopathology is equally distributed and not driven by only a few features. We tested the new histopathological scoring criteria using images of lumbar and coccygeal discs from different IDD models of both sexes, including genetic, needle‐punctured, static compressive models, and natural aging mice spanning neonatal to old age stages. Moreover, disc sections from common histological preparation techniques and stains including H&E, SafraninO/Fast green, and FAST were analyzed to enable better cross‐study comparisons. Fleiss's multi‐rater agreement test shows significant agreement by both experienced and novice multiple raters for all 14 features on several mouse models and sections prepared using various histological techniques. The sensitivity and specificity of the new scoring system was validated using artificial intelligence and supervised and unsupervised machine learning algorithms, including artificial neural networks, k‐means clustering, and principal component analysis. Finally, we applied the new scoring system on established disc degeneration models and demonstrated high sensitivity and specificity of histopathological scoring changes. Overall, the new histopathological scoring system offers the ability to quantify histological changes in mouse models of disc degeneration and regeneration with high sensitivity and specificity. We have developed a new Mouse intErveRtebral disC histopathologY (MERCY) system using a step‐wise approach that included building consensus in the spine community, testing reliability using various mouse disc degeneration models for agreement by multiple raters, validating for high sensitivity and specificity using AI and machine learning algorithms, and applied on established models of murine disc degeneration. Hence, this new system can be broadly applied to quantify mouse IVD histopathology in disc degeneration and regeneration models.

Human cognition and behaviors depend upon the brain's functional connectomes, which vary remarkably across individuals. However, whether and how the functional connectome individual variability architecture is structurally constrained remains largely unknown. Using tractography- and morphometry-based network models, we observed the spatial convergence of structural and functional connectome individual variability, with higher variability in heteromodal association regions and lower variability in primary regions. We demonstrated that functional variability is significantly predicted by a unifying structural variability pattern and that this prediction follows a primary-to-heteromodal hierarchical axis, with higher accuracy in primary regions and lower accuracy in heteromodal regions. We further decomposed group-level connectome variability patterns into individual unique contributions and uncovered the structural-functional correspondence that is associated with individual cognitive traits. These results advance our understanding of the structural basis of individual functional variability and suggest the importance of integrating multimodal connectome signatures for individual differences in cognition and behaviors.

Subtilases (SBTs) are serine peptidases that are found in all three domains of life. As compared with homologs in other Eucarya, plant SBTs are more closely related to archaeal and bacterial SBTs, with which they share many biochemical and structural features. However, in the course of evolution, functional diversification led to the acquisition of novel, plant-specific functions, resulting in the present-day complexity of the plant SBT family. SBTs are much more numerous in plants than in any other organism, and include enzymes involved in general proteolysis as well as highly specific processing proteases. Most SBTs are targeted to the cell wall, where they contribute to the control of growth and development by regulating the properties of the cell wall and the activity of extracellular signaling molecules. Plant SBTs affect all stages of the life cycle as they contribute to embryogenesis, seed development and germination, cuticle formation and epidermal patterning, vascular development, programmed cell death, organ abscission, senescence, and plant responses to their biotic and abiotic environments. In this article we provide a comprehensive picture of SBT structure and function in plants.

An emerging field of human brain imaging deals with the characterization of the connectome, a comprehensive global description of structural and functional connectivity within the human brain. However, the question of how functional and structural connectivity are related has not been fully answered yet. Here, we used different methods to estimate the connectivity between each voxel of the cerebral cortex based on functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data in order to obtain observer-independent functional–structural connectomes of the human brain. Probabilistic fiber-tracking and a novel global fiber-tracking technique were used to measure structural connectivity whereas for functional connectivity, full and partial correlations between each voxel pair's fMRI-timecourses were calculated. For every voxel, two vectors consisting of functional and structural connectivity estimates to all other voxels in the cortex were correlated with each other. In this way, voxels structurally and functionally connected to similar regions within the rest of the brain could be identified. Areas forming parts of the ‘default mode network’ (DMN) showed the highest agreement of structure–function connectivity. Bilateral precuneal and inferior parietal regions were found using all applied techniques, whereas the global tracking algorithm additionally revealed bilateral medial prefrontal cortices and early visual areas. There were no significant differences between the results obtained from full and partial correlations. Our data suggests that the DMN is the functional brain network, which uses the most direct structural connections. Thus, the anatomical profile of the brain seems to shape its functional repertoire and the computation of the whole-brain functional–structural connectome appears to be a valuable method to characterize global brain connectivity within and between populations. •Structure–function connectivity relationship•Multi-modal data fusion•Voxel-wise connectivity analysis•Default mode network•Global fiber-tracking