Exploring Transcriptomic Landscapes in Red Blood Cells, in Their Extracellular Vesicles and on a Single-Cell Level.

Being enucleated, RBCs lack typical transcriptomes, but are known to contain small amounts of diverse long transcripts and microRNAs. However, the exact role and importance of these RNAs are lacking. Shedding of extracellular vesicles (EVs) from the plasma membrane constitutes an integral mechanism of RBC homeostasis, by which RBCs remove unnecessary cytoplasmic content and cell membrane. To study this further, we explored the transcriptomes of RBCs and extracellular vesicles (EVs) of RBCs using next-generation sequencing. Furthermore, we performed single-cell RNA sequencing on RBCs, which revealed that approximately 10% of the cells contained detectable levels of mRNA and cells formed three subpopulations based on their transcriptomes. A decrease in the mRNA quantity was observed across the populations. Qualitative changes included the differences in the globin transcripts and changes in the expression of ribosomal genes. A specific splice form of a long non-coding RNA, Metastasis Associated Lung Adenocarcinoma Transcript 1 (MALAT1), was the most enriched marker in one subpopulation of RBCs, co-expressing with ribosomal structural transcripts. MALAT1 expression was confirmed by qPCR in CD71-enriched reticulocytes, which were also characterized with imaging flow cytometry at the single cell level. Analysis of the RBC transcriptome shows enrichment of pathways and functional categories required for the maturation of reticulocytes and erythrocyte functions. The RBC transcriptome was detected in their EVs, making these transcripts available for intercellular communication in blood.

FinDonor 10 000 study: a cohort to identify iron depletion and factors affecting it in Finnish blood donors.

BACKGROUND AND OBJECTIVES: There is increasing evidence that frequent blood donation depletes the iron stores of some blood donors. The FinDonor 10 000 study was set up to study iron status and factors affecting iron stores in Finnish blood donors. In Finland, iron supplementation for at-risk groups has been in place since the 1980s. MATERIAL AND METHODS: A total of 2584 blood donors (N = 8003 samples) were recruited into the study alongside standard donation at three donation sites in the capital region of Finland between 5/2015 and 12/2017. All participants were asked to fill out a questionnaire about their health and lifestyle. Blood samples were collected from the sample pouch of whole blood collection set, kept in cool temperature and processed centrally. Whole blood count, CRP, ferritin and sTFR were measured from the samples, and DNA was isolated for GWAS studies. RESULTS: Participant demographics, albeit in general similar to the general blood donor population in Finland, indicated some bias towards older and more frequent donors. Participation in the study increased median donation frequency of the donors. Analysis of the effect of time lag from the sampling to the analysis and the time of day when sample was drawn revealed small but significant time-dependent changes. CONCLUSION: The FinDonor cohort now provides us with tools to identify potential donor groups at increased risk of iron deficiency and factors explaining this risk. The increase in donation frequency during the study suggests that scientific projects can be used to increase the commitment of blood donors.

Genome-wide Analysis of STAT3-Mediated Transcription during Early Human Th17 Cell Differentiation.

The development of therapeutic strategies to combat immune-associated diseases requires the molecular mechanisms of human Th17 cell differentiation to be fully identified and understood. To investigate transcriptional control of Th17 cell differentiation, we used primary human CD4+ T cells in small interfering RNA (siRNA)-mediated gene silencing and chromatin immunoprecipitation followed by massive parallel sequencing (ChIP-seq) to identify both the early direct and indirect targets of STAT3. The integrated dataset presented in this study confirms that STAT3 is critical for transcriptional regulation of early human Th17 cell differentiation. Additionally, we found that a number of SNPs from loci associated with immune-mediated disorders were located at sites where STAT3 binds to induce Th17 cell specification. Importantly, introduction of such SNPs alters STAT3 binding in DNA affinity precipitation assays. Overall, our study provides important insights for modulating Th17-mediated pathogenic immune responses in humans.

Retrospective analysis of capillary hemoglobin recovery in nearly 1 200 000 blood donor returns.

Measuring the concentration of capillary hemoglobin (cHb) is a standard procedure before blood donation. To further assess the time period needed for cHb recovery after blood donation and to have a more in-depth understanding of features of recovery, we used data-mining tools in a large, retrospective data pool containing all 1 163 524 donor returns that took place in Finland in 2010 to 2015. The results show that the average recovery times for cHb to return back to the level preceding donation were substantially longer, over 200 days in all age groups, than were the minimum allowed donation intervals. cHb recovery was especially poor in women under the age of 30 who returned to donate soon after the minimum allowed donation interval. It was of interest that frequent donors recovered substantially faster, with the average recovery times of ∼100 days in men and ∼200 days in women, than did infrequent donors, suggesting that there is a subpopulation of donors who can donate frequently without fear of iron deficiency. Return interval in fact explained only 1% of the variation in cHb recovery, which points to unknown, individual features, such as genetic or lifestyle factors, warranting further studies and suggesting that simply extending the allowed donation intervals may not suffice to improve cHb recovery. The study demonstrates that data mining of blood bank records is a powerful tool for depicting features of blood donor population.

Accuracy of Programs for the Determination of Human Leukocyte Antigen Alleles from Next-Generation Sequencing Data.

The human leukocyte antigen (HLA) genes code for proteins that play a central role in the function of the immune system by presenting peptide antigens to T cells. As HLA genes show extremely high genetic polymorphism, HLA typing at the allele level is demanding and is based on DNA sequencing. Determination of HLA alleles is warranted as HLA alleles are major genetic risk factors in autoimmune diseases and are matched in transplantation. Here, we compared the accuracy of several published HLA-typing algorithms that are based on next-generation sequencing (NGS) data. As genome sequencing is becoming increasingly common in research, we wanted to test how well HLA alleles can be deduced from genome data produced in studies with objectives other than HLA typing and in platforms not especially designed for HLA typing. The accuracies were assessed using datasets consisting of NGS data produced using an in-house sequencing platform, including the full 4 Mbp HLA segment, from 94 stem cell transplantation patients and exome sequences from 63 samples of the 1000 Genomes collection. In the patient dataset, none of the software gave perfect results for all the samples and genes when programs were used with the default settings. However, we found that ensemble prediction of the results or modifications of the settings could be used to improve accuracy. For the exome-only data, most of the algorithms did not perform very well. The results indicate that the use of these algorithms for accurate HLA allele determination is not straightforward when based on NGS data not especially targeted to the HLA typing and their accurate use requires HLA expertise.

Graft Immune Cell Composition Associates with Clinical Outcome of Allogeneic Hematopoietic Stem Cell Transplantation in Patients with AML.

Complications of allogeneic hematopoietic stem cell transplantation (HSCT) have been attributed to immune cells transferred into the patient with the graft. However, a detailed immune cell composition of the graft is usually not evaluated. In the present study, we determined the level of variation in the composition of immune cells between clinical HSCT grafts and whether this variation is associated with clinical outcome. Sizes of major immune cell populations in 50 clinical grafts from a single HSCT Centre were analyzed using flow cytometry. A statistical comparison between cell levels and clinical outcomes of HSCT was performed. Overall survival, acute graft-versus-host disease (aGVHD), chronic graft-versus-host disease (cGVHD), and relapse were used as the primary endpoints. Individual HSCT grafts showed considerable variation in their numbers of immune cell populations, including CD123+ dendritic cells and CD34+ cells, which may play a role in GVHD. Acute myeloid leukemia (AML) patients who developed aGVHD were transplanted with higher levels of effector CD3+ T, CD19+ B, and CD123+ dendritic cells than AML patients without aGVHD, whereas grafts with a high CD34+ content protected against aGVHD. AML patients with cGVHD had received grafts with a lower level of monocytes and a higher level of CD34+ cells than those without cGVHD. There is considerable variation in the levels of immune cell populations between HSCT grafts, and this variation is associated with outcomes of HSCT in AML patients. A detailed analysis of the immune cell content of the graft can be used in risk assessment of HSCT.

Season and time of day affect capillary blood hemoglobin level and low hemoglobin deferral in blood donors: analysis in a national blood bank.

BACKGROUND: Low hemoglobin (Hb) is the most common reason for temporary blood donor deferral. However, factors that affect Hb measurement may not portray donor health but reflect external circumstances. STUDY DESIGN AND METHODS: The effects of season, time of day, donor age, ABO, and D on capillary blood Hb level (cHb) and low Hb deferral were analyzed in 1,396,645 donor registrations from the years 2010 to 2014 in a national blood bank. RESULTS: cHb was lower in the summer (July mean 154.1 g/L in men, 139.6 g/L in women) and in the evening (7 pm mean 153.8 g/L in men, 138.9 g/L in women) than in the winter (January mean 156.9 g/L in men, 141.8 g/L in women) and in the morning (11 am mean 157.2 g/L in men, 142.8 g/L in women; all p < 0.0001). This affected donor deferral due to low Hb, with 7.8% donors deferred in July at 7 pm and 1.6% deferred in January at 11 am (p < 0.0001). With age, cHb increased in women and decreased in men. The lowest cHb was observed in blood group A (mean 154.9 g/L in men, 140.3 g/L in women) and the highest in blood group B (mean 156.6 g/L in men, 141.5 g/L in women). D had no practically significant effect on cHb. CONCLUSION: External factors, which do not reflect donor health, affect cHb and donor deferral due to low Hb. These factors should be considered when donor eligibility guidelines and procedures are developed.

Adenosinergic Immunosuppression by Human Mesenchymal Stromal Cells Requires Co-Operation with T cells.

Mesenchymal stem/stromal cells (MSCs) have the capacity to counteract excessive inflammatory responses. MSCs possess a range of immunomodulatory mechanisms, which can be deployed in response to signals in a particular environment and in concert with other immune cells. One immunosuppressive mechanism, not so well-known in MSCs, is mediated via adenosinergic pathway by ectonucleotidases CD73 and CD39. In this study, we demonstrate that adenosine is actively produced from adenosine 5'-monophosphate (AMP) by CD73 on MSCs and MSC-derived extracellular vesicles (EVs). Our results indicate that although MSCs express CD39 at low level and it colocalizes with CD73 in bulge areas of membranes, the most efficient adenosine production from adenosine 5'-triphosphate (ATP) requires co-operation of MSCs and activated T cells. Highly CD39 expressing activated T cells produce AMP from ATP and MSCs produce adenosine from AMP via CD73 activity. Furthermore, adenosinergic signaling plays a role in suppression of T cell proliferation in vitro. In conclusion, this study shows that adenosinergic signaling is an important immunoregulatory mechanism of MSCs, especially in situations where ATP is present in the extracellular environment, like in tissue injury. An efficient production of immunosuppressive adenosine is dependent on the concerted action of CD39-positive immune cells with CD73-positive cells such as MSCs or their EVs.

Identification of global regulators of T-helper cell lineage specification.

BACKGROUND: Activation and differentiation of T-helper (Th) cells into Th1 and Th2 types is a complex process orchestrated by distinct gene activation programs engaging a number of genes. This process is crucial for a robust immune response and an imbalance might lead to disease states such as autoimmune diseases or allergy. Therefore, identification of genes involved in this process is paramount to further understand the pathogenesis of, and design interventions for, immune-mediated diseases. METHODS: We aimed at identifying protein-coding genes and long non-coding RNAs (lncRNAs) involved in early differentiation of T-helper cells by transcriptome analysis of cord blood-derived naïve precursor, primary and polarized cells. RESULTS: Here, we identified lineage-specific genes involved in early differentiation of Th1 and Th2 subsets by integrating transcriptional profiling data from multiple platforms. We have obtained a high confidence list of genes as well as a list of novel genes by employing more than one profiling platform. We show that the density of lineage-specific epigenetic marks is higher around lineage-specific genes than anywhere else in the genome. Based on next-generation sequencing data we identified lineage-specific lncRNAs involved in early Th1 and Th2 differentiation and predicted their expected functions through Gene Ontology analysis. We show that there is a positive trend in the expression of the closest lineage-specific lncRNA and gene pairs. We also found out that there is an enrichment of disease SNPs around a number of lncRNAs identified, suggesting that these lncRNAs might play a role in the etiology of autoimmune diseases. CONCLUSION: The results presented here show the involvement of several new actors in the early differentiation of T-helper cells and will be a valuable resource for better understanding of autoimmune processes.

Cancer-associated ASXL1 mutations may act as gain-of-function mutations of the ASXL1-BAP1 complex.

ASXL1 is the obligate regulatory subunit of a deubiquitinase complex whose catalytic subunit is BAP1. Heterozygous mutations of ASXL1 that result in premature truncations are frequent in myeloid leukemias and Bohring-Opitz syndrome. Here we demonstrate that ASXL1 truncations confer enhanced activity on the ASXL1-BAP1 complex. Stable expression of truncated, hyperactive ASXL1-BAP1 complexes in a haematopoietic precursor cell line results in global erasure of H2AK119Ub, striking depletion of H3K27me3, selective upregulation of a subset of genes whose promoters are marked by both H2AK119Ub and H3K4me3, and spontaneous differentiation to the mast cell lineage. These outcomes require the catalytic activity of BAP1, indicating that they are downstream consequences of H2AK119Ub erasure. In bone marrow precursors, expression of truncated ASXL1-BAP1 complex cooperates with TET2 loss-of-function to increase differentiation to the myeloid lineage in vivo. Our data raise the possibility that ASXL1 truncation mutations confer gain-of-function on the ASXL-BAP1 complex.

Using multi-step proposal distribution for improved MCMC convergence in Bayesian network structure learning.

Bayesian networks have become popular for modeling probabilistic relationships between entities. As their structure can also be given a causal interpretation about the studied system, they can be used to learn, for example, regulatory relationships of genes or proteins in biological networks and pathways. Inference of the Bayesian network structure is complicated by the size of the model structure space, necessitating the use of optimization methods or sampling techniques, such Markov Chain Monte Carlo (MCMC) methods. However, convergence of MCMC chains is in many cases slow and can become even a harder issue as the dataset size grows. We show here how to improve convergence in the Bayesian network structure space by using an adjustable proposal distribution with the possibility to propose a wide range of steps in the structure space, and demonstrate improved network structure inference by analyzing phosphoprotein data from the human primary T cell signaling network.

Draft Genome Sequence of the Hydrogen- and Ethanol-Producing Anaerobic Alkalithermophilic Bacterium Caloramator celer.

Caloramator celer strain JW/YL-NZ35 is a Gram-positive thermophilic, alkalitolerant, and strictly anaerobic bacterium capable of producing hydrogen and ethanol under extreme conditions. The draft genome sequence presented here will provide valuable information to further explore the physiology of this species and its potential for biofuel production.

Global chromatin state analysis reveals lineage-specific enhancers during the initiation of human T helper 1 and T helper 2 cell polarization.

Naive CD4⁺ T cells can differentiate into specific helper and regulatory T cell lineages in order to combat infection and disease. The correct response to cytokines and a controlled balance of these populations is critical for the immune system and the avoidance of autoimmune disorders. To investigate how early cell-fate commitment is regulated, we generated the first human genome-wide maps of histone modifications that reveal enhancer elements after 72 hr of in vitro polarization toward T helper 1 (Th1) and T helper 2 (Th2) cell lineages. Our analysis indicated that even at this very early time point, cell-specific gene regulation and enhancers were at work directing lineage commitment. Further examination of lineage-specific enhancers identified transcription factors (TFs) with known and unknown T cell roles as putative drivers of lineage-specific gene expression. Lastly, an integrative analysis of immunopathogenic-associated SNPs suggests a role for distal regulatory elements in disease etiology.

Genome Sequence of Halanaerobium saccharolyticum subsp. saccharolyticum Strain DSM 6643T, a Halophilic Hydrogen-Producing Bacterium.

Halanaerobium saccharolyticum is a halophilic anaerobic fermentative bacterium capable of producing hydrogen, a potential future energy carrier molecule. The high-quality draft genome of H. saccharolyticum subsp. saccharolyticum strain DSM 6643(T) consists of 24 contigs for 2,873,865 bp with a G+C content of 32.3%.

Structure learning for Bayesian networks as models of biological networks.

Bayesian networks are probabilistic graphical models suitable for modeling several kinds of biological systems. In many cases, the structure of a Bayesian network represents causal molecular mechanisms or statistical associations of the underlying system. Bayesian networks have been applied, for example, for inferring the structure of many biological networks from experimental data. We present some recent progress in learning the structure of static and dynamic Bayesian networks from data.

An integrative computational systems biology approach identifies differentially regulated dynamic transcriptome signatures which drive the initiation of human T helper cell differentiation.

BACKGROUND: A proper balance between different T helper (Th) cell subsets is necessary for normal functioning of the adaptive immune system. Revealing key genes and pathways driving the differentiation to distinct Th cell lineages provides important insight into underlying molecular mechanisms and new opportunities for modulating the immune response. Previous computational methods to quantify and visualize kinetic differential expression data of three or more lineages to identify reciprocally regulated genes have relied on clustering approaches and regression methods which have time as a factor, but have lacked methods which explicitly model temporal behavior. RESULTS: We studied transcriptional dynamics of human umbilical cord blood T helper cells cultured in absence and presence of cytokines promoting Th1 or Th2 differentiation. To identify genes that exhibit distinct lineage commitment dynamics and are specific for initiating differentiation to different Th cell subsets, we developed a novel computational methodology (LIGAP) allowing integrative analysis and visualization of multiple lineages over whole time-course profiles. Applying LIGAP to time-course data from multiple Th cell lineages, we identified and experimentally validated several differentially regulated Th cell subset specific genes as well as reciprocally regulated genes. Combining differentially regulated transcriptional profiles with transcription factor binding site and pathway information, we identified previously known and new putative transcriptional mechanisms involved in Th cell subset differentiation. All differentially regulated genes among the lineages together with an implementation of LIGAP are provided as an open-source resource. CONCLUSIONS: The LIGAP method is widely applicable to quantify differential time-course dynamics of many types of datasets and generalizes to any number of conditions. It summarizes all the time-course measurements together with the associated uncertainty for visualization and manual assessment purposes. Here we identified novel human Th subset specific transcripts as well as regulatory mechanisms important for the initiation of the Th cell subset differentiation.

Chemical castration and anti-androgens induce differential gene expression in prostate cancer.

Endocrine therapy by castration or anti-androgens is the gold standard treatment for advanced prostate cancer. Although it has been used for decades, the molecular consequences of androgen deprivation are incompletely known and biomarkers of its resistance are lacking. In this study, we studied the molecular mechanisms of hormonal therapy by comparing the effect of bicalutamide (anti-androgen), goserelin (GnRH agonist) and no therapy, followed by radical prostatectomy. For this purpose, 28 men were randomly assigned to treatment groups. Freshly frozen specimens were used for gene expression profiling for all known protein-coding genes. An in silico Bayesian modelling tool was used to assess cancer-specific gene expression from heterogeneous tissue specimens. The expression of 128 genes was > two-fold reduced by the treatments. Only 16% of the altered genes were common in both treatment groups. Of the 128 genes, only 24 were directly androgen-regulated genes, according to re-analysis of previous data on gene expression, androgen receptor-binding sites and histone modifications in prostate cancer cell line models. The tumours containing TMPRSS2-ERG fusion showed higher gene expression of genes related to proliferation compared to the fusion-negative tumours in untreated cases. Interestingly, endocrine therapy reduced the expression of one-half of these genes and thus diminished the differences between the fusion-positive and -negative samples. This study reports the significantly different effects of an anti-androgen and a GnRH agonist on gene expression in prostate cancer cells. TMPRSS2-ERG fusion seems to bring many proliferation-related genes under androgen regulation.

Improved triacylglycerol production in Acinetobacter baylyi ADP1 by metabolic engineering.

BACKGROUND: Triacylglycerols are used in various purposes including food applications, cosmetics, oleochemicals and biofuels. Currently the main sources for triacylglycerol are vegetable oils, and microbial triacylglycerol has been suggested as an alternative for these. Due to the low production rates and yields of microbial processes, the role of metabolic engineering has become more significant. As a robust model organism for genetic and metabolic studies, and for the natural capability to produce triacylglycerol, Acinetobacter baylyi ADP1 serves as an excellent organism for modelling the effects of metabolic engineering for energy molecule biosynthesis. RESULTS: Beneficial gene deletions regarding triacylglycerol production were screened by computational means exploiting the metabolic model of ADP1. Four deletions, acr1, poxB, dgkA, and a triacylglycerol lipase were chosen to be studied experimentally both separately and concurrently by constructing a knock-out strain (MT) with three of the deletions. Improvements in triacylglycerol production were observed: the strain MT produced 5.6 fold more triacylglycerol (mg/g cell dry weight) compared to the wild type strain, and the proportion of triacylglycerol in total lipids was increased by 8-fold. CONCLUSIONS: In silico predictions of beneficial gene deletions were verified experimentally. The chosen single and multiple gene deletions affected beneficially the natural triacylglycerol metabolism of A. baylyi ADP1. This study demonstrates the importance of single gene deletions in triacylglycerol metabolism, and proposes Acinetobacter sp. ADP1 as a model system for bioenergetic studies regarding metabolic engineering.

Reconstruction and validation of RefRec: a global model for the yeast molecular interaction network.

Molecular interaction networks establish all cell biological processes. The networks are under intensive research that is facilitated by new high-throughput measurement techniques for the detection, quantification, and characterization of molecules and their physical interactions. For the common model organism yeast Saccharomyces cerevisiae, public databases store a significant part of the accumulated information and, on the way to better understanding of the cellular processes, there is a need to integrate this information into a consistent reconstruction of the molecular interaction network. This work presents and validates RefRec, the most comprehensive molecular interaction network reconstruction currently available for yeast. The reconstruction integrates protein synthesis pathways, a metabolic network, and a protein-protein interaction network from major biological databases. The core of the reconstruction is based on a reference object approach in which genes, transcripts, and proteins are identified using their primary sequences. This enables their unambiguous identification and non-redundant integration. The obtained total number of different molecular species and their connecting interactions is approximately 67,000. In order to demonstrate the capacity of RefRec for functional predictions, it was used for simulating the gene knockout damage propagation in the molecular interaction network in approximately 590,000 experimentally validated mutant strains. Based on the simulation results, a statistical classifier was subsequently able to correctly predict the viability of most of the strains. The results also showed that the usage of different types of molecular species in the reconstruction is important for accurate phenotype prediction. In general, the findings demonstrate the benefits of global reconstructions of molecular interaction networks. With all the molecular species and their physical interactions explicitly modeled, our reconstruction is able to serve as a valuable resource in additional analyses involving objects from multiple molecular -omes. For that purpose, RefRec is freely available in the Systems Biology Markup Language format.

Biocharts: a visual formalism for complex biological systems.

We address one of the central issues in devising languages, methods and tools for the modelling and analysis of complex biological systems, that of linking high-level (e.g. intercellular) information with lower-level (e.g. intracellular) information. Adequate ways of dealing with this issue are crucial for understanding biological networks and pathways, which typically contain huge amounts of data that continue to grow as our knowledge and understanding of a system increases. Trying to comprehend such data using the standard methods currently in use is often virtually impossible. We propose a two-tier compound visual language, which we call Biocharts, that is geared towards building fully executable models of biological systems. One of the main goals of our approach is to enable biologists to actively participate in the computational modelling effort, in a natural way. The high-level part of our language is a version of statecharts, which have been shown to be extremely successful in software and systems engineering. The statecharts can be combined with any appropriately well-defined language (preferably a diagrammatic one) for specifying the low-level dynamics of the pathways and networks. We illustrate the language and our general modelling approach using the well-studied process of bacterial chemotaxis.