Influence of Spino-Pelvic and Postural Alignment Parameters on Gait Kinematics

Introduction: Postural alignment is altered with spine deformities that might occur with age. Alteration of spino-pelvic and postural alignment parameters are known to affect daily life activities such as gait. It is still unknown how spino-pelvic and postural alignment parameters are related to gait kinematics. Research question: To assess the relationships between spino-pelvic/postural alignment parameters and gait kinematics in asymptomatic adults. Methods: 134 asymptomatic subjects (aged 18-59 years) underwent 3D gait analysis, from which kinematics of the pelvis and lower limbs were extracted in the 3 planes. Subjects then underwent full-body biplanar X-rays, from which skeletal 3D reconstructions and spino-pelvic and postural alignment parameters were obtained such as sagittal vertical axis (SVA), center of auditory meatus to hip axis plumbline (CAM-HA), thoracic kyphosis (TK) and radiologic pelvic tilt (rPT). In order to assess the influence of spino-pelvic and postural alignment parameters on gait kinematics a univariate followed by a multivariate analysis were performed. Results: SVA was related to knee flexion during loading response (β = 0.268); CAM-HA to ROM pelvic obliquity (β = -0.19); rPT to mean pelvic tilt (β = -0.185) and ROM pelvic obliquity (β = -0.297); TK to ROM hip flexion/extension in stance (β = -0.17), mean foot progression in stance (β = -0.329), walking speed (β = -0.19), foot off (β = 0.223) and step length (β = -0.181). Significance: This study showed that increasing SVA, CAM-HA, TK and rPT, which is known to occur in adults with spinal deformities, could alter gait kinematics. Increases in these parameters, even in asymptomatic subjects, were related to a retroverted pelvis during gait, a reduced pelvic obliquity and hip flexion/extension mobility, an increased knee flexion during loading response as well as an increase in external foot progression angle. This was associated with a decrease in the walking pace: reduced speed, step length and longer stance phase

Disentangling Direct from Indirect Co-Evolution of Residues in Protein Alignments

Predicting protein structure from primary sequence is one of the ultimate challenges in computational biology. Given the large amount of available sequence data, the analysis of co-evolution, i.e., statistical dependency, between columns in multiple alignments of protein domain sequences remains one of the most promising avenues for predicting residues that are contacting in the structure. A key impediment to this approach is that strong statistical dependencies are also observed for many residue pairs that are distal in the structure. Using a comprehensive analysis of protein domains with available three-dimensional structures we show that co-evolving contacts very commonly form chains that percolate through the protein structure, inducing indirect statistical dependencies between many distal pairs of residues. We characterize the distributions of length and spatial distance traveled by these co-evolving contact chains and show that they explain a large fraction of observed statistical dependencies between structurally distal pairs. We adapt a recently developed Bayesian network model into a rigorous procedure for disentangling direct from indirect statistical dependencies, and we demonstrate that this method not only successfully accomplishes this task, but also allows contacts with weak statistical dependency to be detected. To illustrate how additional information can be incorporated into our method, we incorporate a phylogenetic correction, and we develop an informative prior that takes into account that the probability for a pair of residues to contact depends strongly on their primary-sequence distance and the amount of conservation that the corresponding columns in the multiple alignment exhibit. We show that our model including these extensions dramatically improves the accuracy of contact prediction from multiple sequence alignments

Integrated Analysis of Residue Coevolution and Protein Structure in ABC Transporters

Intraprotein side chain contacts can couple the evolutionary process of amino acid substitution at one position to that at another. This coupling, known as residue coevolution, may vary in strength. Conserved contacts thus not only define 3-dimensional protein structure, but also indicate which residue-residue interactions are crucial to a protein’s function. Therefore, prediction of strongly coevolving residue-pairs helps clarify molecular mechanisms underlying function. Previously, various coevolution detectors have been employed separately to predict these pairs purely from multiple sequence alignments, while disregarding available structural information. This study introduces an integrative framework that improves the accuracy of such predictions, relative to previous approaches, by combining multiple coevolution detectors and incorporating structural contact information. This framework is applied to the ABC-B and ABC-C transporter families, which include the drug exporter P-glycoprotein involved in multidrug resistance of cancer cells, as well as the CFTR chloride channel linked to cystic fibrosis disease. The predicted coevolving pairs are further analyzed based on conformational changes inferred from outward- and inward-facing transporter structures. The analysis suggests that some pairs coevolved to directly regulate conformational changes of the alternating-access transport mechanism, while others to stabilize rigid-body-like components of the protein structure. Moreover, some identified pairs correspond to residues previously implicated in cystic fibrosis

Conserved and variable correlated mutations in the plant MADS protein network

<p>Abstract</p> <p>Background</p> <p>Plant MADS domain proteins are involved in a variety of developmental processes for which their ability to form various interactions is a key requisite. However, not much is known about the structure of these proteins or their complexes, whereas such knowledge would be valuable for a better understanding of their function. Here, we analyze those proteins and the complexes they form using a correlated mutation approach in combination with available structural, bioinformatics and experimental data.</p> <p>Results</p> <p>Correlated mutations are affected by several types of noise, which is difficult to disentangle from the real signal. In our analysis of the MADS domain proteins, we apply for the first time a correlated mutation analysis to a family of interacting proteins. This provides a unique way to investigate the amount of signal that is present in correlated mutations because it allows direct comparison of mutations in various family members and assessing their conservation. We show that correlated mutations in general are conserved within the various family members, and if not, the variability at the respective positions is less in the proteins in which the correlated mutation does not occur. Also, intermolecular correlated mutation signals for interacting pairs of proteins display clear overlap with other bioinformatics data, which is not the case for non-interacting protein pairs, an observation which validates the intermolecular correlated mutations. Having validated the correlated mutation results, we apply them to infer the structural organization of the MADS domain proteins.</p> <p>Conclusion</p> <p>Our analysis enables understanding of the structural organization of the MADS domain proteins, including support for predicted helices based on correlated mutation patterns, and evidence for a specific interaction site in those proteins.</p

Sequence Motifs in MADS Transcription Factors Responsible for Specificity and Diversification of Protein-Protein Interaction

Protein sequences encompass tertiary structures and contain information about specific molecular interactions, which in turn determine biological functions of proteins. Knowledge about how protein sequences define interaction specificity is largely missing, in particular for paralogous protein families with high sequence similarity, such as the plant MADS domain transcription factor family. In comparison to the situation in mammalian species, this important family of transcription regulators has expanded enormously in plant species and contains over 100 members in the model plant species Arabidopsis thaliana. Here, we provide insight into the mechanisms that determine protein-protein interaction specificity for the Arabidopsis MADS domain transcription factor family, using an integrated computational and experimental approach. Plant MADS proteins have highly similar amino acid sequences, but their dimerization patterns vary substantially. Our computational analysis uncovered small sequence regions that explain observed differences in dimerization patterns with reasonable accuracy. Furthermore, we show the usefulness of the method for prediction of MADS domain transcription factor interaction networks in other plant species. Introduction of mutations in the predicted interaction motifs demonstrated that single amino acid mutations can have a large effect and lead to loss or gain of specific interactions. In addition, various performed bioinformatics analyses shed light on the way evolution has shaped MADS domain transcription factor interaction specificity. Identified protein-protein interaction motifs appeared to be strongly conserved among orthologs, indicating their evolutionary importance. We also provide evidence that mutations in these motifs can be a source for sub- or neo-functionalization. The analyses presented here take us a step forward in understanding protein-protein interactions and the interplay between protein sequences and network evolution

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.Peer reviewe

Genome of the anaerobic fungus Orpinomyces sp. strain C1A reveals the unique evolutionary history of a remarkable plant biomass degrader

Anaerobic gut fungi represent a distinct early-branching fungal phylum (Neocallimastigomycota) and reside in the rumen, hindgut, and feces of ruminant and nonruminant herbivores. The genome of an anaerobic fungal isolate, Orpinomyces sp. strain C1A, was sequenced using a combination of Illumina and PacBio single-molecule real-time (SMRT) technologies. The large genome (100.95 Mb, 16,347 genes) displayed extremely low G+C content (17.0%), large noncoding intergenic regions (73.1%), proliferation of microsatellite repeats (4.9%), and multiple gene duplications. Comparative genomic analysis identified multiple genes and pathways that are absent in Dikarya genomes but present in early-branching fungal lineages and/or nonfungal Opisthokonta. These included genes for posttranslational fucosylation, the production of specific intramembrane proteases and extracellular protease inhibitors, the formation of a complete axoneme and intraflagellar trafficking machinery, and a near-complete focal adhesion machinery. Analysis of the lignocellulolytic machinery in the C1A genome revealed an extremely rich repertoire, with evidence of horizontal gene acquisition from multiple bacterial lineages. Experimental analysis indicated that strain C1A is a remarkable biomass degrader, capable of simultaneous saccharification and fermentation of the cellulosic and hemicellulosic fractions in multiple untreated grasses and crop residues examined, with the process significantly enhanced by mild pretreatments. This capability, acquired during its separate evolutionary trajectory in the rumen, along with its resilience and invasiveness compared to prokaryotic anaerobes, renders anaerobic fungi promising agents for consolidated bioprocessing schemes in biofuels production.Peer reviewedMicrobiology and Molecular GeneticsBiosystems and Agricultural Engineerin

A year of genomic surveillance reveals how the SARS-CoV-2 pandemic unfolded in Africa.

The progression of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in Africa has so far been heterogeneous, and the full impact is not yet well understood. In this study, we describe the genomic epidemiology using a dataset of 8746 genomes from 33 African countries and two overseas territories. We show that the epidemics in most countries were initiated by importations predominantly from Europe, which diminished after the early introduction of international travel restrictions. As the pandemic progressed, ongoing transmission in many countries and increasing mobility led to the emergence and spread within the continent of many variants of concern and interest, such as B.1.351, B.1.525, A.23.1, and C.1.1. Although distorted by low sampling numbers and blind spots, the findings highlight that Africa must not be left behind in the global pandemic response, otherwise it could become a source for new variants