Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrated Comparative Genomic Analysis and Phenotypic Profiling of Pseudomonas aeruginosa Isolates From Crude Oil

Pseudomonas aeruginosa is an environmental microorganism that can thrive in diverse ecological niches including plants, animals, water, soil, and crude oil. It also one of the microorganism widely used in tertiary recovery of crude oil and bioremediation. However, the genomic information regarding the mechanisms of survival and adapation of this bacterium in crude oil is still limited. In this study, three Pseudomonads strains (named as IMP66, IMP67, and IMP68) isolated from crude oil were taken for whole-genome sequencing by using a hybridized PacBio and Illumina approach. The phylogeny analysis showed that the three strains were all P. aeruginosa species and clustered in clade 1, the group with PAO1 as a representitive. Subsequent comparative genomic analysis revealed a high degree of individual genomic plasticity, with a probable alkane degradation genomic island, one type I-F CRISPR-Cas system and several prophages integrated into their genomes. Nine genes encoding alkane hydroxylases (AHs) homologs were found in each strain, which might enable these strains to degrade alkane in crude oil. P. aeruginosa can produce rhamnolipids (RLs) biosurfactant to emulsify oil, which enables their survival in crude oil enviroments. Our previous report showed that IMP67 and IMP68 were high RLs producers, while IMP66 produced little RLs. Genomic analysis suggested that their RLs yield was not likely due to differences at genetic level. We then further analyzed the quorum sensing (QS) signal molecules that regulate RLs synthesis. IMP67 and IMP68 produced more N-acyl-homoserine lactones (AHLs) signal molecules than that of PAO1 and IMP66, which could explain their high RLs yield. This study provides evidence for adaptation of P. aeruginosa in crude oil and proposes the potential application of IMP67 and IMP68 in microbial-enhanced oil recovery and bioremediation.
Extracellular Vesicles Derived Human-miRNAs Modulate the Immune System in Type 1 Diabetes

Extracellular vesicles with their molecular cargo can modulate target cell response and may affect the pathogenesis of diseases. The extracellular vesicles containing micro-RNAs (miRNAs), which are often studied as disease biomarkers, but rarely as mediators of the disease development. The role of extracellular vesicles derived miRNAs in type 1 diabetes is currently not well established. We observed a fraction of blood plasma extracellular vesicles positive for membrane proteins potentially associated with insulin-producing beta-cells and identified differentially expressed extracellular vesicles derived miRNAs in individuals with type 1 diabetes. These differentially expressed extracellular vesicles derived human miRNAs in participants with type 1 diabetes and participants with Langerhans islets beta-cells destruction showed the ability to activate TLR7/8 signaling cascade and increase activation as well as cytotoxicity of the effector blood immune cells with cytokine and chemokine release. Our results illustrate extracellular vesicles derived human miRNAs as modulators of the immune system in type 1 diabetes autoimmunity, providing potentially new insight into the pathogenesis of the disease, and novel molecular targets for intervention and type 1 diabetes prevention.
Antifibrotic Effects of a Barbituric Acid Derivative on Liver Fibrosis by Blocking the NF-κB Signaling Pathway in Hepatic Stellate Cells

Hepatic stellate cells (HSCs) are the major profibrogenic cells that promote the pathogenesis of liver fibrosis. The crosstalk between transforming growth factor-β1 (TGF-β1) signaling and lipopolysaccharide (LPS)-induced NF-κB signaling plays a critical role in accelerating liver fibrogenesis. Until now, there have been no FDA-approved drug treatments for liver fibrosis. Barbituric acid derivatives have been used as antiasthmatic drugs in the clinic; however, the effect of barbituric acid derivatives in treating liver fibrosis remains unknown. In this study, we synthesized a series of six barbituric acid (BA) derivatives, and one of the compounds, BA-5, exhibited the best ability to ameliorate TGF-β1-induced HSC activation without overt cytotoxic effects. Then, we treated HSCs and RAW264.7 macrophages with BA-5 to analyze the cross-talk of anti-fibrotic and anti-inflammatory effects. Carbon tetrachloride (CCl4)-induced liver fibrosis mouse model was used to evaluate the therapeutic effects of BA-5. Treatment with BA-5 inhibited TGF-β1-induced α-SMA, collagen1a2, and phosphorylated smad2/3 expression in HSCs. Furthermore, BA-5 treatment reversed the LPS-induced reduction in BAMBI protein and decreased IκBα and NF-κB phosphorylation in HSCs. NF-κB nuclear translocation, MCP-1 secretion, and ICAM-1 expression were also inhibited in BA-5-treated HSCs. Conditioned medium collected from BA-5-treated HSCs showed a reduced ability to activate RAW264.7 macrophages by inhibiting the MAPK pathway. In the mouse model, BA-5 administration reduced CCl4-induced liver damage, liver fibrosis, and F4/80 expression without any adverse effects. In conclusion, our study showed that the barbituric acid derivative BA-5 inhibits HSCs activation and liver fibrosis by blocking both the TGF-β1 and LPS-induced NF-κB signaling pathways and further inhibits macrophages recruitment and activation.
Expression Profile and Prognostic Values of HOXA Family Members in Laryngeal Squamous Cell Cancer

The homeobox A cluster (HOXA) gene family, comprising 11 members, is involved in a wide spectrum of biological functions in human cancers. However, there is little research on the expression profile and prognostic values of HOXA genes in laryngeal squamous cell cancer (LSCC). Based on updated public resources and integrative bioinformatics analysis, we assessed the expression profile and prognostic values of the HOXA family members. Expression and methylation data on HOXA family members were obtained from The Cancer Genome Atlas (TCGA). The prognostic values of HOXA members and clinical features were identified. A gene set enrichment analysis (GSEA) was conducted to explore the mechanism underlying the involvement of HOXA members in LSCC. The associations between tumor immune infiltrating cells (TIICs) and the HOXA family members were evaluated using the Tumor Immune Estimation Resource (TIMER) database. HOXA2 and HOXA4 were downregulated and HOXA7 and HOXA9–13 were upregulated in LSCC. Upregulation of HOXA10, HOXA11, and HOXA13, along with two clinical characteristics (M stage and gender), were associated with a poor LSCC prognosis based on the results of univariate and multivariate Cox proportional hazards regression analyses. Although there were no significant correlations between TIICs and HOXA members, the GSEA results indicated that HOXA members participate in multiple biological processes underlying tumorigenesis. This study comprehensively analyzed the HOXA members, providing insights for further investigation of the HOXA family members as potential targets in LSCC.
Molecular and Low-Resolution Structural Characterization of the Na+-Translocating Glutaconyl-CoA Decarboxylase From Clostridium symbiosum

Some anaerobic bacteria use biotin-dependent Na+-translocating decarboxylases (Bdc) of β-keto acids or their thioester analogs as key enzymes in their energy metabolism. Glutaconyl-CoA decarboxylase (Gcd), a member of this protein family, drives the endergonic translocation of Na+ across the membrane with the exergonic decarboxylation of glutaconyl-CoA (ΔG0’ ≈−30 kJ/mol) to crotonyl-CoA. Here, we report on the molecular characterization of Gcd from Clostridium symbiosum based on native PAGE, size exclusion chromatography (SEC) and laser-induced liquid bead ion desorption mass spectrometry (LILBID-MS). The obtained molecular mass of ca. 400 kDa fits to the DNA sequence-derived mass of 379 kDa with a subunit composition of 4 GcdA (65 kDa), 2 GcdB (35 kDa), GcdC1 (15 kDa), GcdC2 (14 kDa), and 2 GcdD (10 kDa). Low-resolution structural information was achieved from preliminary electron microscopic (EM) measurements, which resulted in a 3D reconstruction model based on negative-stained particles. The Gcd structure is built up of a membrane-spanning base primarily composed of the GcdB dimer and a solvent-exposed head with the GcdA tetramer as major component. Both globular parts are bridged by a linker presumably built up of segments of GcdC1, GcdC2 and the 2 GcdDs. The structure of the highly mobile Gcd complex represents a template for the global architecture of the Bdc family.
Integrated Analysis to Evaluate the Prognostic Value of Signature mRNAs in Glioblastoma Multiforme

Background Gliomas are the most common intracranial tumors and are classified as I–IV. Among them, glioblastoma multiforme (GBM) is the most common invasive glioma with a poor prognosis. New molecular biomarkers that can predict clinical outcomes in GBM patients must be identified, which will help comprehend their pathogenesis and supply personalized treatment. Our research revealed four powerful survival indicators in GBM by reanalyzing microarray data and genetic sequencing data in public databases. Moreover, it unraveled new potential therapeutic targets which could help improve the survival time and quality of life of GBM patients. Materials and Methods: To identify prognostic signatures in GBMs, we analyzed the gene profiling data of GBM and standard brain samples from the Gene Expression Omnibus, including four datasets and RNA sequencing data from The Cancer Genome Atlas (TCGA) containing 152 glioblastoma tissues. We performed the differential analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, weighted gene co-expression network analysis (WGCNA) and Cox regression analysis. Results After differential analysis in GSE12657, GSE15824, GSE42656 and GSE50161, overlapping differentially expressed genes were identified. We identified 110 up-regulated DEGs and 75 down-regulated DEGs in the GBM samples. Significantly enriched subclasses of the GO classification of these genes included mitotic sister chromatid separation, mitotic nuclear division and so on. In KEGG pathway analysis, the most abundant terms were ECM-receptor interaction and protein digestion and absorption. WGCNA analysis was performed on these 185 DEGs in 152 glioblastoma samples obtained from TCGA, and gene co-expression networks were constructed. We then performed a multivariate Cox analysis and established a Cox proportional hazards regression model using the top 20 genes significantly correlated with survival time. We identified a four-protein prognostic signature that could divide patients into high-risk and low-risk groups. Increased expression of SLC12A5, CCL2, IGFBP2, and PDPN was associated with increased risk scores. Finally, the K-M curves confirmed that these genes could be used as independent predictors of survival in patients with glioblastoma. Conclusion Our analytical study identified a set of potential biomarkers that could predict survival and may contribute to successful treatment of GBM patients.
The Evolution of Data Fusion Methodologies Developed to Reconstruct Coronary Artery Geometry From Intravascular Imaging and Coronary Angiography Data: A Comprehensive Review

Understanding the mechanisms that regulate atherosclerotic plaque formation and evolution is a crucial step for developing treatment strategies that will prevent plaque progression and reduce cardiovascular events. Advances in signal processing and the miniaturization of medical devices have enabled the design of multimodality intravascular imaging catheters that allow complete and detailed assessment of plaque morphology and biology. However, a significant limitation of these novel imaging catheters is that they provide two-dimensional (2D) visualization of the lumen and vessel wall and thus they cannot portray vessel geometry and 3D lesion architecture. To address this limitation computer-based methodologies and user-friendly software have been developed. These are able to off-line process and fuse intravascular imaging data with X-ray or computed tomography coronary angiography (CTCA) to reconstruct coronary artery anatomy. The aim of this review article is to summarize the evolution in the field of coronary artery modeling; we thus present the first methodologies that were developed to model vessel geometry, highlight the modifications introduced in revised methods to overcome the limitations of the first approaches and discuss the challenges that need to be addressed, so these techniques can have broad application in clinical practice and research.
Transition of Nano-Architectures Through Self-Assembly of Lipidated β3-Tripeptide Foldamers

β3-peptides consisting exclusively of β3-amino acids adopt a variety of non-natural helical structures and can self-assemble into well-defined hierarchical structures by axial head-to-tail self-assembly resulting in fibrous materials of varying sizes and shapes. To allow control of fiber morphology, a lipid moiety was introduced within a tri-β3-peptide sequence at each of the three amino acid positions and the N-terminus to gain finer control over the lateral assembly of fibers. Depending on the position of the lipid, the self-assembled structures formed either twisted ribbon-like fibers or distinctive multilaminar nanobelts. The nanobelt structures were comprised of multiple layers of peptide fibrils as revealed by puncturing the surface of the nanobelts with an AFM probe. This stacking phenomenon was completely inhibited through changes in pH, indicating that the layer stacking was mediated by electrostatic interactions. Thus, the present study is the first to show controlled self-assembly of these fibrous structures, which is governed by the location of the acyl chain in combination with the 3-point H-bonding motif. Overall, the results demonstrate that the nanostructures formed by the β3-tripeptide foldamers can be tuned via sequential lipidation of N-acetyl β3-tripeptides which control the lateral interactions between peptide fibrils and provide defined structures with a greater homogeneous population.
Kinship Determination in Archeological Contexts Through DNA Analysis

Knowing kinship relations between individuals in archeological contexts is of great importance to understand social habits and structure in past human communities. Archeological and anthropological analyses of burial sites and skeletal remains often allow us to infer connections between individuals, but only genetic analysis can provide a sound determination of kinship. Several case studies are now available in the literature that show the potentiality and limitations of different methodological approaches based on ancient DNA (aDNA). Both experimental and computational strategies for kinship estimation on ancient samples are described in this review and we argue that, within a multidisciplinary approach, kinship inference contributes to the understanding of the biological and cultural patterns that characterized past societies.
SciApps: A Cloud-Based Platform for Analyses and Distribution of the MaizeCODE data

MaizeCODE is a project aimed at identifying and analyzing functional elements in the maize genome. In its initial phase, MaizeCODE assayed up to five tissues from four maize strains (B73, NC350, W22, TIL11) by RNA-Seq, Chip-Seq, RAMPAGE, and small RNA sequencing. To facilitate reproducible science and provide both human and machine access to the MaizeCODE data, we developed SciApps, a cloud-based portal, for analysis and distribution of both raw data and analysis results. Based on the SciApps workflow platform, we generated new components to support the complete cycle of the MaizeCODE data management. These include publicly accessible scientific workflows for reproducible and shareable analysis of various functional data, a RESTful API for batch processing and distribution of data and metadata, a searchable data page that lists each MaizeCODE experiment as a reproducible workflow, and integrated JBrowse genome browser tracks linked with workflows and metadata. The SciApps portal is a flexible platform that allows integration of new analysis tools, workflows, and genomic data from multiple projects. Through metadata and a ready-to-compute cloud-based platform, the portal experience improves access to the MaizeCODE data and facilitates its analysis.
Dynamic Interplay of Host and Pathogens in an Avian Whole-Blood Model

Microbial survival in blood is an essential step toward the development of disseminated diseases and blood stream infections. For poultry, however, little is known about the interactions of host cells and pathogens in blood. We established an ex vivo chicken whole-blood infection assay as a tool to analyze interactions between host cells and three model pathogens, Escherichia coli, Staphylococcus aureus, and Candida albicans. Following a systems biology approach, we complemented the experimental measurements with functional and quantitative immune characteristics by virtual infection modeling. All three pathogens were killed in whole blood, but each to a different extent and with different kinetics. Monocytes, and to a lesser extent heterophils, associated with pathogens. Both association with host cells and transcriptional activation of genes encoding immune-associated functions differed depending on both the pathogen and the genetic background of the chickens. Our results provide first insights into quantitative interactions of three model pathogens with different immune cell populations in avian blood, demonstrating a broad spectrum of different characteristics during the immune response that depends on the pathogen and the chicken line.
Metabolic Reprogramming in Triple-Negative Breast Cancer

Metabolic reprogramming is an emerging hallmark of cancer cells, in which cancer cells exhibit distinct metabolic phenotypes to fuel their proliferation and progression. The significant advancements made in the area of metabolic reprogramming make possible new strategies for overcoming malignant cancer, including triple-negative breast cancer. Triple-negative breast cancer (TNBC) is associated with high histologic grade, aggressive phenotype, and poor prognosis. Even though triple-negative breast cancer patients benefit from standard chemotherapy, they still face high recurrence rates and are more likely to develop resistance to chemotherapeutic drugs. Therefore, there is an urgent need to explore vulnerabilities of triple-negative breast cancer and develop novel therapeutic drugs to improve clinical outcomes for triple-negative breast cancer patients. Metabolic reprogramming may provide promising therapeutic targets for the treatment of triple-negative breast cancer. In this paper, we primarily discuss how triple-negative breast cancer cells reprogram their metabolic phenotype and that of stromal cells in the microenvironment to survive under nutrient-poor conditions. Considering that metastasis and chemoresistance are the main contributors to mortality in triple-negative breast cancer patients, we also focus on the role of metabolic adaption in mediating metastasis and chemoresistance of triple-negative breast cancer tumors.
Iron to Gas: Versatile Multiport Flow-Column Revealed Extremely High Corrosion Potential by Methanogen-Induced Microbiologically Influenced Corrosion (Mi-MIC)

Currently, sulfate-reducing bacteria (SRB) is regarded as the main culprit of microbiologically influenced corrosion (MIC), mainly due to the low reported corrosion rates of other microorganisms. For example, the highest reported corrosion rate for methanogens is 0.065 mm/yr. However, by investigating methanogen-induced microbiologically influenced corrosion (Mi-MIC) using an in-house developed versatile multiport flow test column, extremely high corrosion rates were observed. We analyzed a large set of carbon steel beads, which were sectionally embedded into the test columns as substrates for iron-utilizing methanogen Methanobacterium IM1. After 14 days of operation using glass beads as fillers for section separation, the highest average corrosion rate of Methanobacterium IM1 was 0.2 mm/yr, which doubled that of Desulfovibrio ferrophilus IS5 and Desulfovibrio alaskensis 16109 investigated at the same conditions. At the most corroded region, nearly 80% of the beads lost 1% of their initial weight (fast-corrosion), resulting in an average corrosion rate of 0.2 mm/yr for Methanobacterium IM1-treated columns. When sand was used as filler material to mimic sediment conditions, average corrosion rates for Methanobacterium IM1 increased to 0.3 mm/yr (maximum 0.52 mm/yr) with over 83% of the beads having corrosion rates above 0.3 mm/yr. Scanning electron images of metal coupons extracted from the column showed methanogenic cells were clustered close to the metal surface. Methanobacterium IM1 is a hydrogenotrophic methanogen with higher affinity to metal than H2. Unlike SRB, Methanobacterium IM1 is not restricted to the availability of sulfate concentration in the environment. Thus, the use of the multiport flow column provided a new insight on the corrosion potential of methanogens, particularly in dynamic conditions, that offers new opportunities for monitoring and development of mitigation strategies. Overall, this study shows (1) under certain conditions methanogenic archaea can cause higher corrosion than SRB, (2) specific quantifications, i.e., maximum, average, and minimum corrosion rates can be determined, and (3) that spatial statistical evaluations of MIC can be carried out.
One Step Ahead—Attention Control Capabilities at Baseline Are Associated With the Effectiveness of the Attention Training Technique

Background Attentional control has been observed to play an important role in affective disorders by impacting information processing, the ability to exert top–down control in response to distracting stimuli, and by affecting emotional regulation. Prior studies demonstrated an association between attentional control and response to psychotherapy, thereby identifying attentional control as an interesting prognostic pre-treatment factor. Improving attentional control and flexibility is a cornerstone in metacognitive therapy (MCT), which is trained by the use of the Attentional Training Technique (ATT). However, as of yet, it remains unclear if pre-treatment attentional control is related to the effect of ATT. Methods An aggregated sample of 139 healthy participants [study 1: 85 participants, mean age 23.7 years, previously published (Barth et al., 2019); study 2: 54 participants, mean age 33.7 years, not previously published] performed an attentional performance test battery before and after applying ATT. Before ATT was administered, attentional control was measured using a well-established self-report instrument, i.e., the Attentional Control Scale (ACS; Derryberry and Reed, 2002). ATT was given in 2, 4, or 15 doses and compared to sham ATT. The test battery comprised a selection of established neurocognitive tasks: emotional dot probe, Stroop, 2-back, and dichotic listening. Results Sham ATT showed no interaction with ACS score on performance outcome in all tests. At four doses of ATT, ACS score was associated with training response, i.e., subjects with high self-reported attentional control before training showed the largest improvements post-training (all P-values <0.05; see Figure 3). At 2 and 15 doses of ATT, the ACS score was unrelated to training response. Conclusion This is a first attempt in understanding the optimal dosage in which ATT should be administered dependent on the individual characteristics of each subject pre-training. The current data suggest self-reported attentional control pre-training as a marker to determine an optimal individual ATT training profile. Future studies should investigate if other domains of metacognitions also interact with training outcome and evaluate the extent to which this relationship transfers to clinical samples. If successful, assessing attentional control prior to treatment in clinical samples could be of use regarding personalized therapy plans and treatment outcome.