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Indigenous chickens predominate poultry production in Africa. Although preferred for backyard farming because of their adaptability to harsh tropical environments, these populations suffer from relatively low productivity compared to commercial lines. Genome analyses can unravel the genetic potential of improvement of these birds for both production and resilience traits for the benefit of African poultry farming systems. Here we report whole-genome sequences of 234 indigenous chickens from 24 Ethiopian populations distributed under diverse agro-climatic conditions. The data represents over eight terabytes of paired-end sequences from the Ilumina HiSeqX platform with an average coverage of about 57X. Almost 99% of the sequence reads could be mapped against the chicken reference genome (GRCg6a), confirming the high quality of the data. Variant calling detected around 15 million SNPs, of which about 86% are known variants (i.e., present in public databases), providing further confidence on the data quality. The dataset provides an excellent resource for investigating genetic diversity and local environmental adaptations with important implications for breed improvement and conservation purposes.Network inference is a notoriously challenging problem. Inferred networks are associated with high uncertainty and likely riddled with false positive and false negative interactions. Especially for biological networks we do not have good ways of judging the performance of inference methods against real networks, and instead we often rely solely on the performance against simulated data. Gaining confidence in networks inferred from real data nevertheless thus requires establishing reliable validation methods. Here, we argue that the expectation of mixing patterns in biological networks such as gene regulatory networks offers a reasonable starting point interactions are more likely to occur between nodes with similar biological functions. We can quantify this behaviour using the assortativity coefficient, and here we show that the resulting heuristic, functional assortativity, offers a reliable and informative route for comparing different inference algorithms.Sepsis-induced AKI (acute kidney injury) is considered an inflammation-related disease with high mortality. LPS-induced (Lipopolysaccharide) TLR4-NFκB pathway activation plays an important role in sepsis-induced AKI. Pyroptosis closely associated with inflammation response includes inflammasome formation, caspase1 activation and GSDMD N-terminal fragment cleavage that leads to cell membrane rupture and cell death, which may be related to the pathogenesis of sepsis-induced AKI. MIF (Macrophage migration inhibitory factor), associated with inflammation response, has been proved as a biomarker of sepsis, and perhaps regulate pyroptosis in sepsis-induced AKI. In this study, we focus on investigating the mechanism of MIF promoting pyroptosis in sepsis-induced AKI. MIF and pyroptosis-related proteins were up-regulated in kidney tissue of mice with CLP (cecum ligation puncture) surgery and in LPS-injured human kidney-2 (HK-2) cells. NLRP3 was down-regulated following the suppression of MIF topoisomerase activity by ISO-1 in kidney tissue of CLP mice. Knockdown of MIF alleviated NLRP3 inflammasome mediated pyroptosis in LPS-injured HK-2 cells. Meanwhile, we noted that phosphorylation of p65 was down-regulated by knockdown of MIF. Up-regulation of NLRP3 in response to LPS stimulation could be reversed by JSH-23, an inhibitor of NFκB pathway, but MIF was not affected. In conclusion, up-regulation of MIF in sepsis-induced AKI shows a renal damaged effect that aggravates NLRP3 inflammasome mediated cell pyroptosis through promoting phosphorylation of p65. This study demonstrated a novel mechanism of MIF regulating NLRP3 inflammasome mediated pyroptosis in sepsis-induced AKI.Cell-based immunotherapies can provide safe and effective treatments for various disorders including autoimmunity, cancer, and excessive proinflammatory events in sepsis or viral infections. However, to achieve this goal there is a need for deeper understanding of mechanisms of the intercellular interactions. Regulatory T cells (Tregs) are a lymphocyte subset that maintain peripheral tolerance, whilst mesenchymal stem cells (MSCs) are multipotent nonhematopoietic progenitor cells. Tetrahydropiperine cost Despite coming from different origins, Tregs and MSCs share immunoregulatory properties that have been tested in clinical trials. Here we demonstrate how direct and indirect contact with allogenic MSCs improves Tregs’ potential for accumulation of immunosuppressive adenosine and suppression of conventional T cell proliferation, making them more potent therapeutic tools. Our results also demonstrate that direct communication between Tregs and MSCs is based on transfer of active mitochondria and fragments of plasma membrane from MSCs to Tregs, an event that is HLA-dependent and associates with HLA-C and HLA-DRB1 eplet mismatch load between Treg and MSC donors.As a global health emergency, the rapid spread of the novel coronavirus disease (COVID-19) led to the implementation of widespread restrictions (e.g., quarantine, physical/social distancing measures). However, while these restrictions reduce the viral spread of COVID-19, they may exacerbate behavioural and cognitive symptoms in dementia patients and increase pressure on caregiving. Here, we aimed to assess the impact of COVID-19 and related restrictions on both carers and people living with dementia across the world. We conducted an international survey (Australia, Germany, Spain, and the Netherlands) to assess the impact of COVID-19 on carers and people living with dementia. People with dementia experienced worsened neuropsychiatric symptoms since the outbreak of COVID-19, most commonly, depression, apathy, delusions, anxiety, irritability, and agitation. Regression analyses revealed that limited understanding of the COVID-19 situation and not living with the carer was associated with worsened neuropsychiatric symptoms. Carers also reported a decline in their own mental health, increased stress and reduced social networks as a result of COVID-19 and related restrictions. Regression analyses revealed uncertainty about the future and loneliness were associated with worsened carer mental health. Findings from this study will inform strategies for the development of support services and compassionate protocols that meet the evolving needs of those living with dementia and their carers.Transposable elements (TEs) are robustly silenced by multiple epigenetic marks, but dynamics of crosstalk among these marks remains enigmatic. In Arabidopsis, TEs are silenced by cytosine methylation in both CpG and non-CpG contexts (mCG and mCH) and histone H3 lysine 9 methylation (H3K9me). While mCH and H3K9me are mutually dependent for their maintenance, mCG and mCH/H3K9me are independently maintained. Here, we show that establishment, rather than maintenance, of mCH depends on mCG, accounting for the synergistic colocalization of these silent marks in TEs. When mCG is lost, establishment of mCH is abolished in TEs. mCG also guides mCH in active genes, though the resulting mCH/H3K9me is removed thereafter. Unexpectedly, targeting efficiency of mCH depends on relative, rather than absolute, levels of mCG within the genome, suggesting underlying global negative controls. We propose that local positive feedback in heterochromatin dynamics, together with global negative feedback, drive robust and balanced DNA methylome patterning.This study aims to develop an assumption-free data-driven model to accurately forecast COVID-19 spread. Towards this end, we firstly employed Bayesian optimization to tune the Gaussian process regression (GPR) hyperparameters to develop an efficient GPR-based model for forecasting the recovered and confirmed COVID-19 cases in two highly impacted countries, India and Brazil. However, machine learning models do not consider the time dependency in the COVID-19 data series. Here, dynamic information has been taken into account to alleviate this limitation by introducing lagged measurements in constructing the investigated machine learning models. Additionally, we assessed the contribution of the incorporated features to the COVID-19 prediction using the Random Forest algorithm. Results reveal that significant improvement can be obtained using the proposed dynamic machine learning models. In addition, the results highlighted the superior performance of the dynamic GPR compared to the other models (i.e., Support vector regression, Boosted trees, Bagged trees, Decision tree, Random Forest, and XGBoost) by achieving an averaged mean absolute percentage error of around 0.1%. Finally, we provided the confidence level of the predicted results based on the dynamic GPR model and showed that the predictions are within the 95% confidence interval. This study presents a promising shallow and simple approach for predicting COVID-19 spread.The tumor suppressor P53 plays critical role in preventing cancer. P53 is rarely mutated and remains functional in luminal-type breast cancer(1). According to current knowledge, wild-type P53 function is tightly controlled by posttranslational modifications, such as ubiquitination. Several ubiquitin ligases have been shown to regulate P53 ubiquitination and protein stability. Here, we report that RNF187, a RING family ubiquitin ligase, facilitates breast cancer growth and inhibits apoptosis by modulating P53 signaling. RNF187 expression was elevated in breast cancer and correlated with breast cancer survival only in the P53 wild-type groups. Bioinformatic analysis showed that the expression of RNF187 was negatively correlated with the expression of P53 target genes, such as IGFBP3 and FAS, in breast cancer. RNF187 depletion inhibited breast cancer growth and facilitated cell death. RNA sequencing analysis indicated that RNF187 could be an important modulator of P53 signaling. Further experiments showed that RNF187 interacts with P53 and promotes its degradation by facilitating its polyubiquitination in breast cancer cells. Interestingly, the in vitro ubiquitin assay showed that RNF187 can directly ubiquitinate P53 in a manner independent of MDM2. These findings reveal a novel direct P53 regulator and a potential therapeutic target for breast cancer.We present a data set of 48182 organic semiconductors, constituted of molecules that were prepared with a documented synthetic pathway and are stable in solid state. We based our search on the Cambridge Structural Database, from which we selected semiconductors with a computational funnel procedure. For each entry we provide a set of electronic properties relevant for organic materials research, and the electronic wavefunction for further calculations and/or analyses. This data set has low bias because it was not built from a set of materials designed for organic electronics, and thus it provides an excellent starting point in the search of new applications for known materials, with a great potential for novel physical insight. The data set contains molecules used as benchmarks in many fields of organic materials research, allowing to test the reliability of computational screenings for the desired application, “rediscovering” well-known molecules. This is demonstrated by a series of different applications in the field of organic materials, confirming the potential for the repurposing of known organic molecules.