Habitat conditions and not moss composition mediate microbial community structure in Swiss peatlands.

Peatlands, one of the oldest ecosystems, globally store significant amounts of carbon and freshwater. However, they are under severe threat from human activities, leading to changes in water, nutrient and temperature regimes in these delicate systems. Such shifts can trigger a substantial carbon flux into the atmosphere and diminish the water-holding capacity of peatlands. Microbes associated with moss in peatlands play a crucial role in providing these ecosystem services, which are at risk due to global change. Therefore, understanding the factors influencing microbial composition and function is vital. Our study focused on five peatlands along an altitudinal gradient in Switzerland, where we sampled moss on hummocks containing Sarracenia purpurea. Structural equation modelling revealed that habitat condition was the primary predictor of community structure and directly influenced other environmental variables. Interestingly, the microbial composition was not linked to the local moss species identity. Instead, microbial communities varied significantly between sites due to differences in acidity levels and nitrogen availability. This finding was also mirrored in a co-occurrence network analysis, which displayed a distinct distribution of indicator species for acidity and nitrogen availability. Therefore, peatland conservation should take into account the critical habitat characteristics of moss-associated microbial communities.

Human Sterols Are Overproduced, Stored and Excreted in Yeasts.

Sterols exert a profound influence on numerous cellular processes, playing a crucial role in both health and disease. However, comprehending the effects of sterol dysfunction on cellular physiology is challenging. Consequently, numerous processes affected by impaired sterol biosynthesis still elude our complete understanding. In this study, we made use of yeast strains that produce cholesterol instead of ergosterol and investigated the cellular response mechanisms on the transcriptome as well as the lipid level. The exchange of ergosterol for cholesterol caused the downregulation of phosphatidylethanolamine and phosphatidylserine and upregulation of phosphatidylinositol and phosphatidylcholine biosynthesis. Additionally, a shift towards polyunsaturated fatty acids was observed. While the sphingolipid levels dropped, the total amounts of sterols and triacylglycerol increased, which resulted in 1.7-fold enlarged lipid droplets in cholesterol-producing yeast cells. In addition to internal storage, cholesterol and its precursors were excreted into the culture supernatant, most likely by the action of ABC transporters Snq2, Pdr12 and Pdr15. Overall, our results demonstrate that, similarly to mammalian cells, the production of non-native sterols and sterol precursors causes lipotoxicity in K. phaffii, mainly due to upregulated sterol biosynthesis, and they highlight the different survival and stress response mechanisms on multiple, integrative levels.

A map of mass spectrometry-based in silico fragmentation prediction and compound identification in metabolomics.

Metabolomics, the comprehensive study of the metabolome, and lipidomics-the large-scale study of pathways and networks of cellular lipids-are major driving forces in enabling personalized medicine. Complicated and error-prone data analysis still remains a bottleneck, however, especially for identifying novel metabolites. Comparing experimental mass spectra to curated databases containing reference spectra has been the gold standard for identification of compounds, but constructing such databases is a costly and time-demanding task. Many software applications try to circumvent this process by utilizing cutting-edge advances in computational methods-including quantum chemistry and machine learning-and simulate mass spectra by performing theoretical, so called in silico fragmentations of compounds. Other solutions concentrate directly on experimental spectra and try to identify structural properties by investigating reoccurring patterns and the relationships between them. The considerable progress made in the field allows recent approaches to provide valuable clues to expedite annotation of experimental mass spectra. This review sheds light on individual strengths and weaknesses of these tools, and attempts to evaluate them-especially in view of lipidomics, when considering complex mixtures found in biological samples as well as mass spectrometer inter-instrument variability.

Comparison of acute myeloid leukemia and myelodysplastic syndromes with TP53 aberrations.

TP53 aberrations are found in approximately 10% of patients with acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS) and are considered early driver events affecting leukemia stem cells. In this study, we compared features of a total of 84 patients with these disorders seen at a tertiary cancer center. Clinical and cytogenetic characteristics as well as immunophenotypes of immature blast cells were similar between AML and MDS patients. Median overall survival (OS) was 226 days (95% confidence interval [CI], 131-300) for the entire cohort with an estimated 3-year OS rate of 11% (95% CI, 6-22). OS showed a significant difference between MDS (median, 345 days; 95% CI, 235-590) and AML patients (median, 91 days; 95% CI, 64-226) which is likely due to a different co-mutational pattern as revealed by next-generation sequencing. Transformation of TP53 aberrant MDS occurred in 60.5% of cases and substantially reduced their survival probability. Cox regression analysis revealed treatment class and TP53 variant allele frequency as prognostically relevant parameters but not the TP53-specific prognostic scores EAp53 and RFS. These data emphasize similarities between TP53 aberrant AML and MDS and support previous notions that they should be classified and treated as a distinct disorder.

Comparison and evaluation of integrative methods for the analysis of multilevel omics data: a study based on simulated and experimental cancer data.

Integrative analysis aims to identify the driving factors of a biological process by the joint exploration of data from multiple cellular levels. The volume of omics data produced is constantly increasing, and so too does the collection of tools for its analysis. Comparative studies assessing performance and the biological value of results, however, are rare but in great demand. We present a comprehensive comparison of three integrative analysis approaches, sparse canonical correlation analysis (sCCA), non-negative matrix factorization (NMF) and logic data mining MicroArray Logic Analyzer (MALA), by applying them to simulated and experimental omics data. We find that sCCA and NMF are able to identify differential features in simulated data, while the Logic Data Mining method, MALA, falls short. Applied to experimental data, we show that MALA performs best in terms of sample classification accuracy, and in general, the classification power of prioritized feature sets is high (97.1-99.5% accuracy). The proportion of features identified by at least one of the other methods, however, is approximately 60% for sCCA and NMF and nearly 30% for MALA, and the proportion of features jointly identified by all methods is only around 16%. Similarly, the congruence on functional levels (Gene Ontology, Reactome) is low. Furthermore, the agreement of identified feature sets with curated gene signatures relevant to the investigated disease is modest. We discuss possible reasons for the moderate overlap of identified feature sets with each other and with curated cancer signatures. The R code to create simulated data, results and figures is provided at https://github.com/ThallingerLab/IamComparison.

BMP7 aberrantly induced in the psoriatic epidermis instructs inflammation-associated Langerhans cells.

BACKGROUND: Epidermal hyperplasia represents a morphologic hallmark of psoriatic skin lesions. Langerhans cells (LCs) in the psoriatic epidermis engage with keratinocytes (KCs) in tight physical interactions; moreover, they induce T-cell-mediated immune responses critical to psoriasis. OBJECTIVE: This study sought to improve the understanding of epidermal factors in psoriasis pathogenesis. METHODS: BMP7-LCs versus TGF-ß1-LCs were phenotypically characterized and their functional properties were analyzed using flow cytometry, cell kinetic studies, co-culture with CD4 T cells, and cytokine measurements. Furthermore, immunohistology of healthy and psoriatic skin was performed. Additionally, in vivo experiments with Junf/fJunBf/fK5cre-ERT mice were carried out to assess the role of bone morphogenetic protein (BMP) signaling in psoriatic skin inflammation. RESULTS: This study identified a KC-derived signal (ie, BMP signaling) to promote epidermal changes in psoriasis. Whereas BMP7 is strictly confined to the basal KC layer in the healthy skin, it is expressed at high levels throughout the lesional psoriatic epidermis. BMP7 instructs precursor cells to differentiate into LCs that phenotypically resemble psoriatic LCs. These BMP7-LCs exhibit proliferative activity and increased sensitivity to bacterial stimulation. Moreover, aberrant high BMP signaling in the lesional epidermis is mediated by a KC intrinsic mechanism, as suggested from murine data and clinical outcome after topical antipsoriatic treatment in human patients. CONCLUSIONS: These data indicate that available TGF-ß family members within the lesional psoriatic epidermis preferentially signal through the canonical BMP signaling cascade to instruct inflammatory-type LCs and to promote psoriatic epidermal changes. Targeting BMP signaling might allow to therapeutically interfere with cutaneous psoriatic manifestations.

FXR-dependent Rubicon induction impairs autophagy in models of human cholestasis.

BACKGROUND & AIMS: Cholestasis comprises a spectrum of liver diseases characterized by the accumulation of bile acids. Bile acids and activation of the farnesoid X receptor (FXR) can inhibit autophagy, a cellular self-digestion process necessary for cellular homeostasis and regeneration. In mice, autophagy appears to be impaired in cholestasis and induction of autophagy may reduce liver injury. METHODS: Herein, we explored autophagy in human cholestasis in vivo and investigated the underlying molecular mechanisms in vitro. FXR chromatin immunoprecipitation-sequencing and qPCR were performed in combination with luciferase promoter studies to identify functional FXR binding targets in a human cholestatic liver sample. RESULTS: Autophagic processing appeared to be impaired in patients with cholestasis and in individuals treated with the FXR ligand obeticholic acid (OCA). In vitro, chenodeoxycholic acid and OCA inhibited autophagy at the level of autophagosome to lysosome fusion in an FXR-dependent manner. Rubicon, which inhibits autophago-lysosomal maturation, was identified as a direct FXR target that is induced in cholestasis and by FXR-agonistic bile acids. Genetic inhibition of Rubicon reversed the bile acid-induced impairment of autophagic flux. In contrast to OCA, ursodeoxycholic acid (UDCA), which is a non-FXR-agonistic bile acid, induced autophagolysosome formation independently of FXR, enhanced autophagic flux and was associated with reduced Rubicon levels. CONCLUSION: In models of human cholestasis, autophagic processing is impaired in an FXR-dependent manner, partly resulting from the induction of Rubicon. UDCA is a potent inducer of hepatic autophagy. Manipulating autophagy and Rubicon may represent a novel treatment concept for cholestatic liver diseases. LAY SUMMARY: Autophagy, a cellular self-cleansing process, is impaired in various forms of human cholestasis. Bile acids, which accumulate in cholestatic liver disease, induce Rubicon, a protein that inhibits proper execution of autophagy. Ursodeoxycholic acid, which is the first-line treatment option for many cholestatic liver diseases, induces hepatic autophagy along with reducing Rubicon.

Deciphering lipid structures based on platform-independent decision rules.

We achieve automated and reliable annotation of lipid species and their molecular structures in high-throughput data from chromatography-coupled tandem mass spectrometry using decision rule sets embedded in Lipid Data Analyzer (LDA; http://genome.tugraz.at/lda2). Using various low- and high-resolution mass spectrometry instruments with several collision energies, we proved the method's platform independence. We propose that the software's reliability, flexibility, and ability to identify novel lipid molecular species may now render current state-of-the-art lipid libraries obsolete.

The CXCR4-CXCL12-Axis Is of Prognostic Relevance in DLBCL and Its Antagonists Exert Pro-Apoptotic Effects In Vitro.

In tumor cells of more than 20 different cancer types, the CXCR4-CXCL12-axis is involved in multiple key processes including proliferation, survival, migration, invasion, and metastasis. Since data on this axis in diffuse large B cell lymphoma (DLBCL) are inconsistent and limited, we comprehensively studied the CXCR4-CXCL12-axis in our DLBCL cohort as well as the effects of CXCR4 antagonists on lymphoma cell lines in vitro. In DLBCL, we observed a 140-fold higher CXCR4 expression compared to non-neoplastic controls, which was associated with poor clinical outcome. In corresponding bone marrow biopsies, we observed a correlation of CXCL12 expression and lymphoma infiltration rate as well as a reduction of CXCR4 expression in remission of bone marrow involvement after treatment. Additionally, we investigated the effects of three CXCR4 antagonists in vitro. Therefore, we used AMD3100 (Plerixafor), AMD070 (Mavorixafor), and WKI, the niacin derivative of AMD070, which we synthesized. WK1 demonstrated stronger pro-apoptotic effects than AMD070 in vitro and induced expression of pro-apoptotic genes of the BCL2-family in CXCR4-positive lymphoma cell lines. Finally, WK1 treatment resulted in the reduced expression of JNK-, ERK1/2- and NF-κB/BCR-target genes. These data indicate that the CXCR4-CXCL12-axis impacts the pathogenesis of DLBCL and represents a potential therapeutic target in aggressive lymphomas.

Deletion of Adipose Triglyceride Lipase Links Triacylglycerol Accumulation to a More-Aggressive Phenotype in A549 Lung Carcinoma Cells.

Adipose triglyceride lipase (ATGL) catalyzes the rate limiting step in triacylglycerol breakdown in adipocytes but is expressed in most tissues. The enzyme was shown to be lost in many human tumors, and its loss may play a role in early stages of cancer development. Here, we report that loss of ATGL supports a more-aggressive cancer phenotype in a model system in which ATGL was deleted in A549 lung cancer cells by CRISPR/Cas9. We observed that loss of ATGL led to triacylglycerol accumulation in lipid droplets and higher levels of cellular phospholipid and bioactive lipid species (lyso- and ether-phospholipids). Label-free quantitative proteomics revealed elevated expression of the pro-oncogene SRC kinase in ATGL depleted cells, which was also found on mRNA level and confirmed on protein level by Western blot. Consistently, higher expression of phosphorylated (active) SRC (Y416 phospho-SRC) was observed in ATGL-KO cells. Cells depleted of ATGL migrated faster, which was dependent on SRC kinase activity. We propose that loss of ATGL may thus increase cancer aggressiveness by activation of pro-oncogenic signaling via SRC kinase and increased levels of bioactive lipids.

Dimension reduction techniques for the integrative analysis of multi-omics data.

State-of-the-art next-generation sequencing, transcriptomics, proteomics and other high-throughput 'omics' technologies enable the efficient generation of large experimental data sets. These data may yield unprecedented knowledge about molecular pathways in cells and their role in disease. Dimension reduction approaches have been widely used in exploratory analysis of single omics data sets. This review will focus on dimension reduction approaches for simultaneous exploratory analyses of multiple data sets. These methods extract the linear relationships that best explain the correlated structure across data sets, the variability both within and between variables (or observations) and may highlight data issues such as batch effects or outliers. We explore dimension reduction techniques as one of the emerging approaches for data integration, and how these can be applied to increase our understanding of biological systems in normal physiological function and disease.

Methanol independent induction in Pichia pastoris by simple derepressed overexpression of single transcription factors.

Carbon source regulated promoters are well-studied standard tools for controlling gene expression. Acquiring control over the natural regulation of promoters is important for metabolic engineering and synthetic biology applications. In the commonly used protein production host Komagataella phaffii (Pichia pastoris), methanol-inducible promoters are used because of their tight regulation and exceptional strength. Yet, induction with toxic and flammable methanol can be a considerable safety risk and cannot be applied in many existing fermentation plants. Here we studied new regulatory circuits based on the most frequently used alcohol oxidase 1 promoter (PAOX1 ), which is tightly repressed in presence of repressing carbon sources and strongly induced by methanol. We compared different overexpression strategies for putative carbon source dependent regulators identified by a homology search in related yeasts and previously published literature in order to convert existing methanol dependent expression strains into methanol free systems. While constitutive overexpression showed only marginal or detrimental effects, derepressed expression (activated when the repressing carbon source is depleted) showed that three transcription factors (TFs) are single handedly suitable to strongly activate PAOX1 in P. pastoris without relying on any specifically engineered host strains. Transcriptome analyses demonstrated that Mxr1, Mit1, and Prm1 regulate partly overlapping and unique sets of genes. Derepressed overexpression of a single TF was sufficient to retrofit existing PAOX1 based expression strains into glucose/glycerol regulated, methanol-free systems. Given the wide applicability of carbon source regulated promoters, the simplicity and low cost of controlling carbon source feed rates in large scale bioreactors, similar approaches as in P. pastoris may also be useful in other organisms.

Molecular Profiling of Phagocytic Immune Cells in Anopheles gambiae Reveals Integral Roles for Hemocytes in Mosquito Innate Immunity.

The innate immune response is highly conserved across all eukaryotes and has been studied in great detail in several model organisms. Hemocytes, the primary immune cell population in mosquitoes, are important components of the mosquito innate immune response, yet critical aspects of their biology have remained uncharacterized. Using a novel method of enrichment, we isolated phagocytic granulocytes and quantified their proteomes by mass spectrometry. The data demonstrate that phagocytosis, blood-feeding, and Plasmodium falciparum infection promote dramatic shifts in the proteomic profiles of An. gambiae granulocyte populations. Of interest, large numbers of immune proteins were induced in response to blood feeding alone, suggesting that granulocytes have an integral role in priming the mosquito immune system for pathogen challenge. In addition, we identify several granulocyte proteins with putative roles as membrane receptors, cell signaling, or immune components that when silenced, have either positive or negative effects on malaria parasite survival. Integrating existing hemocyte transcriptional profiles, we also compare differences in hemocyte transcript and protein expression to provide new insight into hemocyte gene regulation and discuss the potential that post-transcriptional regulation may be an important component of hemocyte gene expression. These data represent a significant advancement in mosquito hemocyte biology, providing the first comprehensive proteomic profiling of mosquito phagocytic granulocytes during homeostasis blood-feeding, and pathogen challenge. Together, these findings extend current knowledge to further illustrate the importance of hemocytes in shaping mosquito innate immunity and their principal role in defining malaria parasite survival in the mosquito host.

Human germ/stem cell-specific gene TEX19 influences cancer cell proliferation and cancer prognosis.

BACKGROUND: Cancer/testis (CT) genes have expression normally restricted to the testis, but become activated during oncogenesis, so they have excellent potential as cancer-specific biomarkers. Evidence is starting to emerge to indicate that they also provide function(s) in the oncogenic programme. Human TEX19 is a recently identified CT gene, but a functional role for TEX19 in cancer has not yet been defined. METHODS: siRNA was used to deplete TEX19 levels in various cancer cell lines. This was extended using shRNA to deplete TEX19 in vivo. Western blotting, fluorescence activated cell sorting and immunofluorescence were used to study the effect of TEX19 depletion in cancer cells and to localize TEX19 in normal testis and cancer cells/tissues. RT-qPCR and RNA sequencing were employed to determine the changes to the transcriptome of cancer cells depleted for TEX19 and Kaplan-Meier plots were generated to explore the relationship between TEX19 expression and prognosis for a range of cancer types. RESULTS: Depletion of TEX19 levels in a range of cancer cell lines in vitro and in vivo restricts cellular proliferation/self-renewal/reduces tumour volume, indicating TEX19 is required for cancer cell proliferative/self-renewal potential. Analysis of cells depleted for TEX19 indicates they enter a quiescent-like state and have subtle defects in S-phase progression. TEX19 is present in both the nucleus and cytoplasm in both cancerous cells and normal testis. In cancer cells, localization switches in a context-dependent fashion. Transcriptome analysis of TEX19 depleted cells reveals altered transcript levels of a number of cancer-/proliferation-associated genes, suggesting that TEX19 could control oncogenic proliferation via a transcript/transcription regulation pathway. Finally, overall survival analysis of high verses low TEX19 expressing tumours indicates that TEX19 expression is linked to prognostic outcomes in different tumour types. CONCLUSIONS: TEX19 is required to drive cell proliferation in a range of cancer cell types, possibly mediated via an oncogenic transcript regulation mechanism. TEX19 expression is linked to a poor prognosis for some cancers and collectively these findings indicate that not only can TEX19 expression serve as a novel cancer biomarker, but may also offer a cancer-specific therapeutic target with broad spectrum potential.

MetExtract II: A Software Suite for Stable Isotope-Assisted Untargeted Metabolomics.

Stable isotope labeling (SIL) techniques have the potential to enhance different aspects of liquid chromatography-high-resolution mass spectrometry (LC-HRMS)-based untargeted metabolomics methods including metabolite detection, annotation of unknown metabolites, and comparative quantification. In this work, we present MetExtract II, a software toolbox for detection of biologically derived compounds. It exploits SIL-specific isotope patterns and elution profiles in LC-HRMS(/MS) data. The toolbox consists of three complementary modules: M1 (AllExtract) uses mixtures of uniformly highly isotope-enriched and native biological samples for selective detection of the entire accessible metabolome. M2 (TracExtract) is particularly suited to probe the metabolism of endogenous or exogenous secondary metabolites and facilitates the untargeted screening of tracer derivatives from concurrently metabolized native and uniformly labeled tracer substances. With M3 (FragExtract), tandem mass spectrometry (MS/MS) fragments of corresponding native and uniformly labeled ions are evaluated and automatically assigned with putative sum formulas. Generated results can be graphically illustrated and exported as a comprehensive data matrix that contains all detected pairs of native and labeled metabolite ions that can be used for database queries, metabolome-wide internal standardization, and statistical analysis. The software, associated documentation, and sample data sets are freely available for noncommercial use at http://metabolomics-ifa.boku.ac.at/metextractII .

NR4A1-mediated apoptosis suppresses lymphomagenesis and is associated with a favorable cancer-specific survival in patients with aggressive B-cell lymphomas.

NR4A1 (Nur77) and NR4A3 (Nor-1) function as tumor suppressor genes as demonstrated by the rapid development of acute myeloid leukemia in the NR4A1 and NR4A3 knockout mouse. The aim of our study was to investigate NR4A1 and NR4A3 expression and function in lymphoid malignancies. We found a vastly reduced expression of NR4A1 and NR4A3 in chronic lymphocytic B-cell leukemia (71%), in follicular lymphoma (FL, 70%), and in diffuse large B-cell lymphoma (DLBCL, 74%). In aggressive lymphomas (DLBCL and FL grade 3), low NR4A1 expression was significantly associated with a non-germinal center B-cell subtype and with poor overall survival. To investigate the function of NR4A1 in lymphomas, we overexpressed NR4A1 in several lymphoma cell lines. Overexpression of NR4A1 led to a higher proportion of lymphoma cells undergoing apoptosis. To test the tumor suppressor function of NR4A1 in vivo, the stable lentiviral-transduced SuDHL4 lymphoma cell line harboring an inducible NR4A1 construct was further investigated in xenografts. Induction of NR4A1 abrogated tumor growth in the NSG mice, in contrast to vector controls, which formed massive tumors. Our data suggest that NR4A1 has proapoptotic functions in aggressive lymphoma cells and define NR4A1 as a novel gene with tumor suppressor properties involved in lymphomagenesis.

Curation of the genome annotation of Pichia pastoris (Komagataella phaffii) CBS7435 from gene level to protein function.

As manually curated and non-automated BLAST analysis of the published Pichia pastoris genome sequences revealed many differences between the gene annotations of the strains GS115 and CBS7435, RNA-Seq analysis, supported by proteomics, was performed to improve the genome annotation. Detailed analysis of sequence alignment and protein domain predictions were made to extend the functional genome annotation to all P. pastoris sequences. This allowed the identification of 492 new ORFs, 4916 hypothetical UTRs and the correction of 341 incorrect ORF predictions, which were mainly due to the presence of upstream ATG or erroneous intron predictions. Moreover, 175 previously erroneously annotated ORFs need to be removed from the annotation. In total, we have annotated 5325 ORFs. Regarding the functionality of those genes, we improved all gene and protein descriptions. Thereby, the percentage of ORFs with functional annotation was increased from 48% to 73%. Furthermore, we defined functional groups, covering 25 biological cellular processes of interest, by grouping all genes that are part of the defined process. All data are presented in the newly launched genome browser and database available at www.pichiagenome.org In summary, we present a wide spectrum of curation of the P. pastoris genome annotation from gene level to protein function.

Comprehensive genome and epigenome characterization of CHO cells in response to evolutionary pressures and over time.

The most striking characteristic of CHO cells is their adaptability, which enables efficient production of proteins as well as growth under a variety of culture conditions, but also results in genomic and phenotypic instability. To investigate the relative contribution of genomic and epigenetic modifications towards phenotype evolution, comprehensive genome and epigenome data are presented for six related CHO cell lines, both in response to perturbations (different culture conditions and media as well as selection of a specific phenotype with increased transient productivity) and in steady state (prolonged time in culture under constant conditions). Clear transitions were observed in DNA-methylation patterns upon each perturbation, while few changes occurred over time under constant conditions. Only minor DNA-methylation changes were observed between exponential and stationary growth phase; however, throughout a batch culture the histone modification pattern underwent continuous adaptation. Variation in genome sequence between the six cell lines on the level of SNPs, InDels, and structural variants is high, both upon perturbation and under constant conditions over time. The here presented comprehensive resource may open the door to improved control and manipulation of gene expression during industrial bioprocesses based on epigenetic mechanisms. Biotechnol. Bioeng. 2016;113: 2241-2253. © 2016 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc.