Distinguishing three subtypes of hematopoietic cells based on gene expression profiles using a support vector machine

Osteoprogenitor-GMP crosstalk underpins solid tumor-induced systemic immunosuppression and persists after tumor removal

Remote tumors disrupt the bone marrow (BM) ecosystem (BME), eliciting the overproduction of BM-derived immunosuppressive cells. However, the underlying mechanisms remain poorly understood. Herein, we characterized breast and lung cancer-induced BME shifts pre- and post-tumor removal. Remote tumors progressively lead to osteoprogenitor (OP) expansion, hematopoietic stem cell dislocation, and CD41- granulocyte-monocyte progenitor (GMP) aggregation. The tumor-entrained BME is characterized by co-localization between CD41- GMPs and OPs. OP ablation abolishes this effect and diminishes abnormal myeloid overproduction. Mechanistically, HTRA1 carried by tumor-derived small extracellular vesicles upregulates MMP-13 in OPs, which in turn induces the alterations in the hematopoietic program. Importantly, these effects persist post-surgery and continue to impair anti-tumor immunity. Conditional knockout or inhibition of MMP-13 accelerates immune reinstatement and restores the efficacies of immunotherapies. Therefore, tumor-induced systemic effects are initiated by OP-GMP crosstalk that outlasts tumor burden, and additional treatment is required to reverse these effects for optimal therapeutic efficacy.

Retinoid X receptor promotes hematopoietic stem cell fitness and quiescence and preserves hematopoietic homeostasis

Hematopoietic stem cells (HSCs) balance self-renewal and differentiation to maintain hematopoietic fitness throughout life. In steady-state conditions, HSC exhaustion is prevented by the maintenance of most HSCs in a quiescent state, with cells entering the cell cycle only occasionally. HSC quiescence is regulated by retinoid and fatty-acid ligands of transcriptional factors of the nuclear retinoid X receptor (RXR) family. Herein, we show that dual deficiency for hematopoietic RXRα and RXRβ induces HSC exhaustion, myeloid cell/megakaryocyte differentiation, and myeloproliferative-like disease. RXRα and RXRβ maintain HSC quiescence, survival, and chromatin compaction; moreover, transcriptome changes in RXRα;RXRβ-deficient HSCs include premature acquisition of an aging-like HSC signature, MYC pathway upregulation, and RNA intron retention. Fitness loss and associated RNA transcriptome and splicing alterations in RXRα;RXRβ-deficient HSCs are prevented by Myc haploinsufficiency. Our study reveals the critical importance of RXRs for the maintenance of HSC fitness and their protection from premature aging.

Epigenetic and Transcriptomic Programming of HSC Quiescence Signaling in Large for Gestational Age Neonates

Excessive fetal growth is associated with DNA methylation alterations in human hematopoietic stem and progenitor cells (HSPC), but their functional impact remains elusive. We implemented an integrative analysis combining single-cell epigenomics, single-cell transcriptomics, and in vitro analyses to functionally link DNA methylation changes to putative alterations of HSPC functions. We showed in hematopoietic stem cells (HSC) from large for gestational age neonates that both DNA hypermethylation and chromatin rearrangements target a specific network of transcription factors known to sustain stem cell quiescence. In parallel, we found a decreased expression of key genes regulating HSC differentiation including EGR1, KLF2, SOCS3, and JUNB. Our functional analyses showed that this epigenetic programming was associated with a decreased ability for HSCs to remain quiescent. Taken together, our multimodal approach using single-cell (epi)genomics showed that human fetal overgrowth affects hematopoietic stem cells' quiescence signaling via epigenetic programming.

Machine learning techniques for mortality prediction in critical traumatic patients: anatomic and physiologic variables from the RETRAUCI study

Background

Interest in models for calculating the risk of death in traumatic patients admitted to ICUs remains high. These models use variables derived from the deviation of physiological parameters and/or the severity of anatomical lesions with respect to the affected body areas. Our objective is to create different predictive models of the mortality of critically traumatic patients using machine learning techniques.

Methods

We used 9625 records from the RETRAUCI database (National Trauma Registry of 52 Spanish ICUs in the period of 2015–2019). Hospital mortality was 12.6%. Data on demographic variables, affected anatomical areas and physiological repercussions were used. The Weka Platform was used, along with a ten-fold cross-validation for the construction of nine supervised algorithms: logistic regression binary (LR), neural network (NN), sequential minimal optimization (SMO), classification rules (JRip), classification trees (CT), Bayesian networks (BN), adaptive boosting (ADABOOST), bootstrap aggregating (BAGGING) and random forest (RFOREST). The performance of the models was evaluated by accuracy, specificity, precision, recall, F-measure, and AUC.

Results

In all algorithms, the most important factors are those associated with traumatic brain injury (TBI) and organic failures. The LR finds thorax and limb injuries as independent protective factors of mortality. The CT generates 24 decision rules and uses those related to TBI as the first variables (range 2.0–81.6%). The JRip detects the eight rules with the highest risk of mortality (65.0–94.1%). The NN model uses a hidden layer of ten nodes, which requires 200 weights for its interpretation. The BN find the relationships between the different factors that identify different patient profiles. Models with the ensemble methodology (ADABOOST, BAGGING and RandomForest) do not have greater performance. All models obtain high values ​​in accuracy, specificity, and AUC, but obtain lower values ​​in recall. The greatest precision is achieved by the SMO model, and the BN obtains the best recall, F-measure, and AUC.

Conclusion

Machine learning techniques are useful for creating mortality classification models in critically traumatic patients. With clinical interpretation, the algorithms establish different patient profiles according to the relationship between the variables used, determine groups of patients with different evolutions, and alert clinicians to the presence of rules that indicate the greatest severity.

Current Trends on Hemopoietic Stem Cells

Advances in single-cell technology and genetic mouse models have resulted in the identification of new types of hemopoietic stem cells (HSC), resulting in baffling observations, suggesting a reconsideration of the long-held notion that all hematopoietic cells in the adult are derived from HSCs. The existence of long-lived HSC-independent hematopoiesis has led to the conclusion that despite the single hierarchical differentiation route that generates functional blood types, other differentiation routes exist in-vivo. Heterogeneity in the HSC population and the evolving knowledge around HSC has translated to it's improved application as a therapeutic tool for various blood disorders. The reprogramming of non-hematopoietic somatic and mature blood cells to pluripotency with their subsequent differentiation into hematopoietic stem cells/progenitor cells and the introduction of new generation sequencing holds the potential for the resolution of ambiguities involved in HSC bone marrow transplantation. There is a change in the paradigm for HSC transplantation donor selection. Donor choice favors haploidentical HCT than cord blood. This review provides a general overview of the current events around hemopoietic stem cells, with emphasis on the rising trend of HSC transplantation, especially haploidentical stem cell transplantation.

Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms

Small nucleolar RNAs (snoRNAs) are a new type of functional small RNAs involved in the chemical modifications of rRNAs, tRNAs, and small nuclear RNAs. It is reported that they play important roles in tumorigenesis via various regulatory modes. snoRNAs can both participate in the regulation of methylation and pseudouridylation and regulate the expression pattern of their host genes. This research investigated the expression pattern of snoRNAs in eight major cancer types in TCGA via several machine learning algorithms. The expression levels of snoRNAs were first analyzed by a powerful feature selection method, Monte Carlo feature selection (MCFS). A feature list and some informative features were accessed. Then, the incremental feature selection (IFS) was applied to the feature list to extract optimal features/snoRNAs, which can make the support vector machine (SVM) yield best performance. The discriminative snoRNAs included HBII-52-14, HBII-336, SNORD123, HBII-85-29, HBII-420, U3, HBI-43, SNORD116, SNORA73B, SCARNA4, HBII-85-20, etc., on which the SVM can provide a Matthew’s correlation coefficient (MCC) of 0.881 for predicting these eight cancer types. On the other hand, the informative features were fed into the Johnson reducer and repeated incremental pruning to produce error reduction (RIPPER) algorithms to generate classification rules, which can clearly show different snoRNAs expression patterns in different cancer types. The analysis results indicated that extracted discriminative snoRNAs can be important for identifying cancer samples in different types and the expression pattern of snoRNAs in different cancer types can be partly uncovered by quantitative recognition rules.

HIV infection alters the human epigenetic landscape

Many complex diseases or traits are the results of both genetic and environmental factors. The environmental factors affect the human body by modifying its epigenetics, which controls the activity of genomes without mutating it. Viral infection is one of the common environmental factors for complex diseases. For example, the human immunodeficiency virus (HIV) infection can cause acquired immune deficiency syndrome (AIDS), HBV, and HCV infections are associated with hepatocellular carcinoma, and human papillomavirus infection is a causal factor in cervical carcinoma. In this study, to investigate how HIV infection affects DNA methylation, we analyzed the blood DNA methylation data of 485 512 sites in 44 HIV- and 142 HIV + patients. Several advanced computational methods were applied to identify the core distinctive features that were different between the HIV patients and the healthy controls. These methods can be used for differentiating HIV-infected patients from uninfected ones. These core distinctive DNA methylation features were confirmed to be functionally connected to premature aging and abnormal immune regulation, two typical pathological symptoms of HIV infection, revealing the potential regulatory mechanisms of HIV infection on the DNA methylation status of the host cells and provided novel insights on the pathogenesis of HIV infection and AIDS.