bluuebunny/crossref_metadata_2025_split huggingface dataset:
Human Skeletal Muscle Fiber Heterogeneity Beyond Myosin Heavy Chains
Authors: Moreno-Justicia Roger , Van der Stede Thibaux , Stocks Ben , Laitila Jenni , Seaborne Robert A , Van de Loock Alexia , Lievens Eline , Samodova Diana , Marín-Arraiza Leyre , Dmytriyeva Oksana , Browaeys Robin , Van Vossel Kim , Moesgaard Lukas , Yigit Nurten , Anckaert Jasper , Weyns Anneleen , Van Thienen Ruud , Sahl Ronni E , Zanoteli Edmar , Lawlor Michael W , Wierer Michael , Mestdagh Pieter , Vandesompele Jo , Ochala Julien , Hostrup Morten , Derave Wim , Deshmukh Atul S 
Published: September 2023

AbstractSkeletal muscle is an inherently heterogenous tissue comprised primarily of myofibers, which are historically classified into three distinct fiber types in humans: one “slow” (type 1) and two “fast” (type 2A and type 2X), delineated by the expression of myosin heavy chain isoforms (MYHs). However, heterogeneity between and within traditional fiber types remains underexplored. Indeed, whether MYHs are the main classifiers of skeletal muscle fibers has not been examined in an unbiased manner. Through the development and application of novel transcriptomic and proteomic workflows, applied to 1050 and 1038 single muscle fibers from humanvastus lateralis, respectively, we show that MYHs are not the only principal drivers of skeletal muscle fiber heterogeneity. Instead, metabolic, ribosomal, and cell junction proteins are a source of multi-dimensional variation between skeletal muscle fibers. Furthermore, whilst slow and fast fiber clusters can be identified, described by their contractile and metabolic profiles, our data suggests that type 2X fibers are not phenotypically distinct to other fast fibers at an omics level. Moreover, MYH-based classifications do not adequately describe the phenotype of skeletal muscle fibers in one of the most common genetic muscle diseases, nemaline myopathy, with fibers shifting towards a non-oxidative phenotype independently of MYH-based fiber type. We also characterize novel transcriptomic and proteomic features of slow and fast skeletal muscle fibers, including identifying several muscle fiber type-specific polypeptides, termed microproteins, encoded by transcripts annotated as non-coding RNA. Overall, our data indicates that skeletal muscle fiber heterogeneity is multi-dimensional with sources of variation beyond myosin heavy chain isoforms.
Lupus IgA1 autoantibodies synergize with IgG to enhance pDC responses to RNA-containing immune complexes
Authors: Waterman Hayley R. , Dufort Matthew J. , Posso Sylvia E. , Ni Minjian , Li Lucy Z. , Zhu Chengsong , Raj Prithvi , Smith Kelly D. , Buckner Jane H. , Hamerman Jessica A. 
Published: September 2023

AbstractAutoantibodies to nuclear antigens are hallmarks of the autoimmune disease systemic lupus erythematosus (SLE) where they contribute to pathogenesis. However, there remains a gap in our knowledge regarding how different isotypes of autoantibodies contribute to disease, including the production of the critical type I interferon (IFN) cytokines by plasmacytoid dendritic cells (pDCs) in response to immune complexes (ICs). We focused on IgA, which is the second most prevalent isotype in serum, and along with IgG is deposited in glomeruli in lupus nephritis. Here, we show that individuals with SLE have IgA autoantibodies against most nuclear antigens, correlating with IgG against the same antigen. We investigated whether IgA autoantibodies against a major SLE autoantigen, Smith ribonucleoproteins (Sm/RNPs), play a role in IC activation of pDCs. We found that pDCs express the IgA-specific Fc receptor, FcαR, and there was a striking ability of IgA1 autoantibodies to synergize with IgG in RNA-containing ICs to generate robust pDC IFNα responses. pDC responses to these ICs required both FcαR and FcγRIIa, showing a potent synergy between these Fc receptors. Sm/RNP IC binding to and internalization by pDCs were greater when ICs contained both IgA1 and IgG. pDCs from individuals with SLE had higher binding of IgA1-containing ICs and higher expression of FcαR than pDCs from healthy control individuals. Whereas pDC FcαR expression correlated with blood ISG signature in SLE, TLR7 agonists, but not IFNα, upregulated pDC FcαR expression in vitro. Together, we show a new mechanism by which IgA1 autoantibodies contribute to SLE pathogenesis.One Sentence SummaryIgA1 autoantibodies synergize with IgG in RNA-containing immune complexes to generate robust pDC IFNα responses in a FcαR receptor dependent manner.
Costs of antibiotic resistance genes depend on host strain and environment and can influence community composition
Authors: Lai Huei-Yi , Cooper Tim F. 
Published: September 2023

AbstractAntibiotic resistance genes (ARGs) is a major contributor to increasing levels of antibiotic resistance in clinical and agricultural settings. ARGs are strongly selected in environments containing corresponding antibiotics, but it is less clear how ARGs are maintained in environments where antibiotic selection might be weak or sporadic. In particular, few studies have directly estimated the effect of ARGs on host fitness in the absence of direct selection. To the extent that ARGs impose costs, it is not clear if these are fixed or might depend on the host strain, perhaps marking some ARG-host combinations as low-cost reservoirs that can act to maintain ARGs in the absence of antibiotic selection. We quantified the fitness effects of six ARGs in each of 11 diverseEscherichiaspp. strains in two environments. While three ARGs(blaTEM-116,cat, anddfrA5, encoding resistance to β-lactam antibiotics, chloramphenicol, and trimethoprim, respectively) imposed an overall cost, all ARGs had an effect in at least one host strain, reflecting a significant ARG effect-by-strain interaction effect. A simulation model predicts that these interactions cause the ecological success of ARGs to depend on available host strains, and, to a lesser extent, for successful host strains to depend on the ARGs present in a community. Together, these results indicate the importance of considering ARG effects over different host strains, especially the potential of reservoir strains that allow resistance to persist in the absence of direct selection, in efforts to understand resistance dynamics.ImportanceAntibiotic resistance is a major and increasing public health concern. Resistance occurs through a variety of mechanisms but commonly involves bacterial strains acquiring antibiotic resistance genes (ARGs) encoded by mobile elements. It is obvious and well-documented that ARGs will be selected in bacteria that are exposed to the antibiotics they confer resistance to. ARGs can also confer costs to bacteria—in environments that do not contain antibiotics, these costs can lead to the loss of ARGs. We show that ARG costs can be significant and that they depend on the host bacterial strain and the environment in which strains are grown. This dependence creates host-environment refuges for many ARGs, allowing them to be maintained in the absence of direct selection.
Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine
Authors: DeGroat William , Abdelhalim Habiba , Patel Kush , Mendhe Dinesh , Zeeshan Saman , Ahmed Zeeshan 
Published: September 2023

AbstractPersonalized interventions are deemed vital given the intricate characteristics, advancement, inherent genetic composition, and diversity of cardiovascular diseases (CVDs). The appropriate utilization of artificial intelligence (AI) and machine learning (ML) methodologies can yield novel understandings of CVDs, enabling improved personalized treatments through predictive analysis and deep phenotyping. In this study, we proposed and employed a novel approach combining traditional statistics and a nexus of cutting-edge AI/ML techniques to identify significant biomarkers for our predictive engine by analyzing the complete transcriptome of CVD patients. After robust gene expression data pre-processing, we utilized three statistical tests (Pearson correlation, Chi-square test, and ANOVA) to assess the differences in transcriptomic expression and clinical characteristics between healthy individuals and CVD patients. Next, the Recursive Feature Elimination (RFE) classifier assigned rankings to transcriptomic features based on their relation to the case-control variable. The top ten percent of commonly observed significant biomarkers were evaluated using four unique ML classifiers (Random Forest, Support Vector Machine, Xtreme Gradient Boosting Decision Trees, and k-Nearest Neighbors). After optimizing hyperparameters, the ensembled models, which were implemented using a soft voting classifier, accurately differentiated between patients and healthy individuals. We have uncovered 18 transcriptomic biomarkers that are highly significant in the CVD population that were used to predict disease with up to 96% accuracy. Additionally, we cross-validated our results with clinical records collected from patients in our cohort. The identified biomarkers served as potential indicators for early detection of CVDs. With its successful implementation, our newly developed predictive engine provides a valuable framework for identifying patients with CVDs based on their biomarker profiles.
Mitochondrial transplantation and its impact on infectious disease progression: a pilot study
Authors: Benson Tom , Patel Samir P. , Albensi Benedict C. , Mahajan Vinit B. , Adlimoghaddam Aida , Sikora Sergey , Saito Hiroshi 
Published: September 2023

ABSTRACTBACKGROUNDMitochondrial transplantation has recently gained prominence as a novel technique primarily focused on addressing ischemiareperfusion injuries and rare mitochondrial mutation diseases. Platelets, abundant in the bloodstream, play a crucial role in immune function. Upon activation, platelets release mitochondria encapsulated within extracellular vesicles, here referred to as “mitlets”. These mitlets exhibit a preference for being internalized by immune cells circulating in the bloodstream, enhancing their cellular energetics. Herein, we hypothesized that the transplantation of mitlets between young animals and aged animals may exert a significant influence on the progression of infectious diseases.STUDY DESIGN AND METHODSIn this study, murine models of Influenza H1N1 infection and sepsis were employed to investigate disease dynamics. Specifically, mitlets isolated from young and healthy mice were transplanted into cohorts of mice of the same age afflicted by H1N1 infection, or into aged mice subjected to polymicrobial infection and sepsis. Survival outcomes and the quantification of cytokine levels were assessed across experimental groups to elucidate the potential therapeutic effects of mitlet transplantation.RESULTSIn the matched-age H1N1 infection model, as predicted, mitlet transplantation did not yield a statistically significant improvement in survival, although it did show a trend towards a reduction in the circulating inflammatory cytokine burden. In the young-to-old sepsis model, the transplantation of mitlets was associated with a significant enhancement in survival rates and a substantial reduction in bacterial loads and circulating cytokine levels.DISCUSSIONOur findings suggest that mitochondrial transplantation may constitute a safe and promising avenue for enhancing the immune system’s capacity to counter infectious threats. This pilot investigation sets the stage for further exploration. It is plausible that in the future, immune senescence resulting from diminished mitochondrial energy production could be ameliorated through such transplantation interventions. As a consequence, this approach holds substantial potential as a novel immunotherapeutic strategy for the management of infectious diseases.
The Effect of Amniotic Fluid On Haıir Follicle Growth
Authors: Tumentemur Gamze , Aygun Elif Ganime , Yurtsever Bulut , Cakirsoy Didem , Ovali Ercument 
Published: September 2023

AbstractHuman amniotic fluid stem cell (hAFSC) exhibit significantly as a new treatment modality in hair loss, wound, scar and regenerative plastic surgery/dermatology. The difference of our study from previous amniotic fluid studies on the hair growth model was studied through the direct effect of human amniotic fluid (hAF). In our study, amniotic fluid was made acellular, pooled and freezed, making it more standardized in terms of its contents. Therefore, the present study is aimed at investigating the efficacy and safety of amniotic fluid in hair loss model on rat. Using the hair loss model rats, we investigated the therapeutic potential of freezing amniotic fluid (FAF) and freezing gama irradiated amniotic fluid (FAFI). Our results showed that FAF and especially FAFI increased number of total hair follicles and accelerated number of transition to anagenic hair follicle. We observed that increased rate of arginase+1(Arg+1)/CD68 activity around the hair follicles treated with hAF. Our study suggests that FAFI may represent a safe and effective tool for increasing hair follicle and accelerating to anagen stage during hair loss model.
Thermodynamic Stabilization of Human Frataxin
Authors: Núñez-Franco Reyes , Torres-Mozas Angel , Navo Claudio D. , Schedlbauer Andreas , Azkargorta Mikel , Iloro Ibon , Elortza Félix , Ortega Gabriel , Millet Oscar , Peccati Francesca , Jiménez-Osés Gonzalo 
Published: September 2023

ABSTRACTRecombinant proteins and antibodies are routinely used as drugs to treat prevalent diseases such as diabetes or cancer, while enzyme replacement and gene therapies are the main therapeutic intervention lines in rare diseases. In protein-based therapeutics, optimizedin vivostability is key as intrinsic denaturation and intracellular proteostatic degradation will limit potency, particularly in treatments requiring a sustained action, while clearance mechanisms may limit the amount of circulating protein.In vivostability is ultimately correlated with the intrinsic thermodynamic stability of the biomolecule, but this is difficult to optimize because it often goes at the expense of reducing protein activity. Here, we have usedin silicoengineering approaches to thermodynamically stabilize human frataxin, a small mitochondrial protein that acts as an allosteric activator for the biosynthesis of Fe-S clusters, whose genetically-driven impairment results in a rare disease known as Friedreich ataxia. Specifically, we developed an efficient thermostability engineering computational approach that combines information on amino acid conservation, the Rosetta energy function, and two recent artificial intelligence tools – AlphaFold and ProteinMPNN – to produce thermodynamically stabilized variants of human frataxin. Such protein variants rescued the large destabilization exerted by well-known pathological mutations, with an increase over 20 °C in the melting temperature and a thermodynamic stabilization of more than 3 kcal·mol-1at the physiological temperature. This stability surplus is translated into an enhanced resistance to proteolysis, while maintaining the protein fully functional. This case-study highlights the power of our combined computational approach to generate optimized variants, adequate for protein-based therapeutics.
Identification of cell types, states and programs by learning gene set representations
Authors: Hediyeh-zadeh Soroor , Whitfield Holly J. , Kharbanda Malvika , Curion Fabiola , Bhuva Dharmesh D. , Theis Fabian J. , Davis Melissa J. 
Published: September 2023

AbstractAs single cell molecular data expand, there is an increasing need for algorithms that efficiently query and prioritize gene programs, cell types and states in single-cell sequencing data, particularly in cell atlases. Here we present scDECAF, a statistical learning algorithm to identify cell types, states and programs in single-cell gene expression data using vector representation of gene sets, which improves biological interpretation by selecting a subset of most biologically relevant programs. We applied scDECAF to scRNAseq data from PBMC, Lung, Pancreas, Brain and slide-tags snRNA of human prefrontal cortex for automatic cell type annotation. We demonstrate that scDECAF can recover perturbed gene programs in Lupus PBMC cells stimulated with IFNbeta and TGFBeta-induced cells undergoing epithelial-to-mesenchymal transition. scDECAF delineates patient-specific heterogeneity in cellular programs in Ovarian Cancer data. Using a healthy PBMC reference, we apply scDECAF to a mapped query PBMC COVID-19 case-control dataset and identify multicellular programs associated with severe COVID-19. scDECAF can improve biological interpretation and complement reference mapping analysis, and provides a method for gene set and pathway analysis in single cell gene expression data.
SARS-CoV-2 Omicron infection augments the magnitude and durability of systemic and mucosal immunity in triple-dose CoronaVac recipients
Authors: Chen Yuxin , Zhao Tiantian , Chen Lin , Jiang Guozhi , Geng Yu , Li Wanting , Yin Shengxia , Tong Xin , Tao Yue , Ni Jun , Lu Qiuhan , Ning Mingzhe , Wu Chao 
Published: September 2023

AbstractBackgroundThe inactivated whole-virion vaccine, CoronaVac, is one of the most widely used coronavirus disease 2019 (COVID-19) vaccines worldwide. There is a paucity of data indicating the durability of the immune response and the impact of immune imprinting induced by CoronaVac upon Omicron breakthrough infection.MethodsIn this prospective cohort study, 41 recipients of triple-dose CoronaVac and 14 unvaccinated individuals were recruited. We comprehensively profiled adaptive immune parameters in both groups, including spike-specific immunoglobulin (Ig) G and IgA titers, neutralizing activity, B cells, follicular helper T (Tfh) cells, CD4+and CD8+T cells, and their memory subpopulations at 12 months after the third booster dose and at 4 weeks and 20 weeks after Omicron BA.5 infection.ResultsTwelve months after the third CoronaVac vaccination, spike-specific antibody and cellular responses were detectable in most vaccinated individuals. BA.5 infection significantly augmented the magnitude, cross-reactivity and durability of serum neutralization activities, Fc-mediated phagocytosis, and nasal spike-specific IgA responses, memory B cells, activated Tfh cells memory CD4+ T cells, and memory CD8+ T cells for both the ancestral strain and Omicron subvariants, compared to unvaccinated individuals. Notably, the increase in BA.5-specific immunity after breakthrough infection was consistently higher than for the ancestral strain, suggesting no evidence of immune imprinting. Immune landscape analyses showed vaccinated individuals have better synchronization of multiple immune components than unvaccinated individuals upon heterologous SARS-CoV-2 infection.ConclusionOur data provides detailed insight into the protective role of inactivated COVID-19 vaccine in shaping humoral and cellular immune responses to heterologous Omicron infection.Trial registrationClinicalTrials.govNCT05680896FundingThis study was supported by the National Natural Science Foundation of China (92269118, 92269205), Nanjing Important Science & Technology Specific Projects (2021-11005), Scientific Research Project of Jiangsu Health Commission (M2022013), Clinical Trials from the Affiliated Drum Tower Hospital, Medical School of Nanjing University (2021-LCYJ-PY-9), and Jiangsu graduate practice innovation project (JX22013929).
Bayesian optimization of separation gradients to maximize the performance of untargeted LC-MS
Authors: Yu Huaxu , Biswas Puja , Rideout Elizabeth , Cao Yankai , Huan Tao 
Published: September 2023

AbstractLiquid chromatography (LC) with gradient elution is a routine practice for separating complex chemical mixtures in mass spectrometry (MS)-based untargeted analysis. Despite its prevalence, systematic optimization of LC gradients has remained challenging. Here we develop a Bayesian optimization method, BAGO, for autonomous and efficient LC gradient optimization. BAGO is an active learning strategy that discovers the optimal gradient using limited experimental data. From over 100,000 plausible gradients, BAGO locates the optimal LC gradient within ten sample analyses. We validated BAGO on six biological studies of different sample matrices and LC columns, showing that BAGO can significantly improve quantitative performance, tandem MS spectral coverage, and spectral purity. For instance, the optimized gradient increases the count of annotated compounds meeting quantification criteria by up to 48.5%. Furthermore, applying BAGO in aDrosophilametabolomics study, an additional 57 metabolites and 126 lipids were annotated. The BAGO algorithms were implemented into user-friendly software for everyday laboratory practice and a Python package for its flexible extension.
HEART rate variability biofeedback breathing programme for LOng Covid dysautonomia (HEARTLOC): results of a feasibility study
Authors: Corrado J , Iftekhar N , Halpin SJ , Li M , Tarrant R , Grimaldi J , Simms AD , O’Connor RJ , Casson AJ , Sivan M 
Published: September 2023

AbstractIntroductionPost-covid-19 syndrome, or Long covid (LC) refers to symptoms persisting 12 weeks after Covid-19 infection. LC comprises a wide range of dysautonomia symptoms, including fatigue, breathlessness, palpitations, dizziness, pain and brain fog. This study tested the feasibility and estimated the efficacy, of a Heart Rate Variability Biofeedback (HRV-B) technique via a standardised slow diaphragmatic breathing programme in individuals with LC.Methods and AnalysisLC patients underwent a 4-week HRV-B intervention for 10 minutes twice daily for a total of 4 weeks using the Polar H10 ECG (Electrocardiogram) chest strap and Elite HRV phone application. Outcome measures C19-YRSm (Yorkshire Rehabilitation Scale modified), EQ5D-5L (EuroQol 5 Dimensions), Composite Autonomic Symptom Score (COMPASS-31), WHO Disability Assessment Schedule (WHODAS), and Root Mean Square of Successive Differences between heartbeats (RMSSD) using a Fitbit device were completed before and after the intervention. The study was pre-registered atclinicaltrials.govNCT05228665.Results13 participants (54% female, 46% male) completed the study with high levels of data completeness and adherence. There was a statistically significant improvement in C19YRS-m (p=0.001), EQ5D Global Health Score (p=0.009), COMPASS-31 (p=0.007), RMSSD (p=0.047) and EHODAS (p=0.02). Qualitative feedback suggested participants were able to use it independently, were satisfied with the intervention, and reported beneficial effects from the intervention.ConclusionThis is the first study in the literature to report that HRV-B is a feasible intervention for LC and seems to be potentially improving symptoms of LC and dysautonomia.Article summaryStrengths and limitations of the studyTo our knowledge, this is the first study of HRV-B in long covid and dysautonomia and has shown the feasibility of a novel technology-based intervention in a home setting.There was a statistically significant improvement in LC symptoms, functional ability, quality of life and dysautonomia scores.The study provides an estimation of efficacy which will determine the sample size for a larger controlled trial in LC and dysautonomia.The main limitation of this study is the small sample size and uncontrolled design which might not give us an accurate estimate of efficacy.The sample was predominantly Caucasian participants, 54% of whom were female.The take-up for HRV-B in those with a lack of experience in using digital technology and those from less privileged backgrounds is unknown.