Toward a predictive understanding of epithelial cell death.

Epithelial cell death is highly prevalent during development and tissue homeostasis. While we have a rather good understanding of the molecular regulators of programmed cell death, especially for apoptosis, we still fail to predict when, where, how many and which specific cells will die in a tissue. This likely relies on the much more complex picture of apoptosis regulation in a tissular and epithelial context, which entails cell autonomous but also non-cell autonomous factors, diverse feedback and multiple layers of regulation of the commitment to apoptosis. In this review, we illustrate this complexity of epithelial apoptosis regulation by describing these different layers of control, all demonstrating that local cell death probability is a complex emerging feature. We first focus on non-cell autonomous factors that can locally modulate the rate of cell death, including cell competition, mechanical input and geometry as well as systemic effects. We then describe the multiple feedback mechanisms generated by cell death itself. We also outline the multiple layers of regulation of epithelial cell death, including the coordination of extrusion and regulation occurring downstream of effector caspases. Eventually, we propose a roadmap to reach a more predictive understanding of cell death regulation in an epithelial context.

LncReader: identification of dual functional long noncoding RNAs using a multi-head self-attention mechanism.

Long noncoding ribonucleic acids (RNAs; LncRNAs) endowed with both protein-coding and noncoding functions are referred to as 'dual functional lncRNAs'. Recently, dual functional lncRNAs have been intensively studied and identified as involved in various fundamental cellular processes. However, apart from time-consuming and cell-type-specific experiments, there is virtually no in silico method for predicting the identity of dual functional lncRNAs. Here, we developed a deep-learning model with a multi-head self-attention mechanism, LncReader, to identify dual functional lncRNAs. Our data demonstrated that LncReader showed multiple advantages compared to various classical machine learning methods using benchmark datasets from our previously reported cncRNAdb project. Moreover, to obtain independent in-house datasets for robust testing, mass spectrometry proteomics combined with RNA-seq and Ribo-seq were applied in four leukaemia cell lines, which further confirmed that LncReader achieved the best performance compared to other tools. Therefore, LncReader provides an accurate and practical tool that enables fast dual functional lncRNA identification.

Temperature-Sensitive Substrate and Product Binding Underlie Temperature-Compensated Phosphorylation in the Clock.

Temperature compensation is a striking feature of the circadian clock. Here we investigate biochemical mechanisms underlying temperature-compensated, CKIδ-dependent multi-site phosphorylation in mammals. We identify two mechanisms for temperature-insensitive phosphorylation at higher temperature: lower substrate affinity to CKIδ-ATP complex and higher product affinity to CKIδ-ADP complex. Inhibitor screening of ADP-dependent phosphatase activity of CKIδ identified aurintricarboxylic acid (ATA) as a temperature-sensitive kinase activator. Docking simulation of ATA and mutagenesis experiment revealed K224D/K224E mutations in CKIδ that impaired product binding and temperature-compensated primed phosphorylation. Importantly, K224D mutation shortens behavioral circadian rhythms and changes the temperature dependency of SCN's circadian period. Interestingly, temperature-compensated phosphorylation was evolutionary conserved in yeast. Molecular dynamics simulation and X-ray crystallography demonstrate that an evolutionally conserved CKI-specific domain around K224 can provide a structural basis for temperature-sensitive substrate and product binding. Surprisingly, this domain can confer temperature compensation on a temperature-sensitive TTBK1. These findings suggest the temperature-sensitive substrate- and product-binding mechanisms underlie temperature compensation.

Integrative systems biology of wheat susceptibility to Fusarium graminearum uncovers a conserved gene regulatory network and identifies master regulators targeted by fungal core effectors.

BACKGROUND: Plant diseases are driven by an intricate set of defense mechanisms counterbalanced by the expression of host susceptibility factors promoted through the action of pathogen effectors. In spite of their central role in the establishment of the pathology, the primary components of plant susceptibility are still poorly understood and challenging to trace especially in plant-fungal interactions such as in Fusarium head blight (FHB) of bread wheat. Designing a system-level transcriptomics approach, we leveraged the analysis of wheat responses from a susceptible cultivar facing Fusarium graminearum strains of different aggressiveness and examined their constancy in four other wheat cultivars also developing FHB. RESULTS: In this study, we describe unexpected differential expression of a conserved set of transcription factors and an original subset of master regulators were evidenced using a regulation network approach. The dual-integration with the expression data of pathogen effector genes combined with database mining, demonstrated robust connections with the plant molecular regulators and identified relevant candidate genes involved in plant susceptibility, mostly able to suppress plant defense mechanisms. Furthermore, taking advantage of wheat cultivars of contrasting susceptibility levels, a refined list of 142 conserved susceptibility gene candidates was proposed to be necessary host's determinants for the establishment of a compatible interaction. CONCLUSIONS: Our findings emphasized major FHB determinants potentially controlling a set of conserved responses associated with susceptibility in bread wheat. They provide new clues for improving FHB control in wheat and also could conceivably leverage further original researches dealing with a broader spectrum of plant pathogens.

Librator: a platform for the optimized analysis, design, and expression of mutable influenza viral antigens.

Artificial mutagenesis and protein engineering have laid the foundation for antigenic characterization and universal vaccine design for influenza viruses. However, many methods used in this process require manual sequence editing and protein expression, limiting their efficiency and utility in high-throughput applications. More streamlined in silico tools allowing researchers to properly analyze and visualize influenza viral protein sequences with accurate nomenclature are necessary to improve antigen design and productivity. To address this need, we developed Librator, a system for analyzing and designing custom protein sequences of influenza virus hemagglutinin (HA) and neuraminidase (NA) glycoproteins. Within Librator's graphical interface, users can easily interrogate viral sequences and phylogenies, visualize antigen structures and conservation, mutate target residues and design custom antigens. Librator also provides optimized fragment design for Gibson Assembly of HA and NA expression constructs based on peptide conservation of all historical HA and NA sequences, ensuring fragments are reusable and compatible across related subtypes, thereby promoting reagent savings. Finally, the program facilitates single-cell immune profiling, epitope mapping of monoclonal antibodies and mosaic protein design. Using Librator-based antigen construction, we demonstrate that antigenicity can be readily transferred between HA molecules of H3, but not H1, lineage viruses. Altogether, Librator is a valuable tool for analyzing influenza virus HA and NA proteins and provides an efficient resource for optimizing recombinant influenza antigen synthesis.

Exploring potential ion channel targets for rheumatoid arthritis: combination of network analysis and gene expression analysis.

Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by chronic inflammation of the synovial membrane that leads to the destruction of cartilage and bone. Currently, pharmacological targeting of ion channels is being increasingly recognized as an attractive and feasible strategy for the treatment of RA. The present work employs a network analysis approach to predict the most promising ion channel target for potential RA-treating drugs. A protein-protein interaction map was generated for 343 genes associated with inflammation in RA and ion channel genes using Search Tool for the Retrieval of Interacting Genes and visualized using Cytoscape. Based on the betweenness centrality and traffic values as key topological parameters, 17 hub nodes were identified, including FOS (9800.85), tumor necrosis factor (3654.60), TGFB1 (3305.75), and VEGFA (3052.88). The backbone network constructed with these 17 hub genes was intensely analyzed to identify the most promising ion channel target using network analyzer. Calcium permeating ion channels, especially store-operated calcium entry channels, and their associated regulatory proteins were found to highly interact with RA inflammatory hub genes. This significant ion channel target for RA identified by theoretical and statistical studies was further validated by a pilot case-control gene expression study. Experimental verification of the above findings in 75 RA cases and 25 controls showed increased ORAI1 expression. Thus, with a combination of network analysis approach and gene expression studies, we have explored potential targets for RA treatment.

Unveiling chronic spontaneous urticaria pathophysiology through systems biology.

BACKGROUND: Chronic spontaneous urticaria (CSU) is a rare, heterogeneous, severely debilitating, and often poorly controlled skin disease resulting in an itchy eruption that can be persistent. Antihistamines and omalizumab, an anti-IgE mAb, are the only licensed therapies. Although CSU pathogenesis is not yet fully understood, mast cell activation through the IgE:high-affinity IgE receptor (FcεRI) axis appears central to the disease process. OBJECTIVE: We sought to model CSU pathophysiology and identify in silico the mechanism of action of different CSU therapeutic strategies currently in use or under development. METHODS: Therapeutic performance mapping system technology, based on systems biology and machine learning, was used to create a CSU interactome validated with gene expression data from patients with CSU and a CSU model that was used to evaluate CSU pathophysiology and the mechanism of action of different therapeutic strategies. RESULTS: Our models reflect the known role of mast cell activation as a central process of CSU pathophysiology, as well as recognized roles for different therapeutic strategies in this and other innate and adaptive immune processes. They also allow determining similarities and differences between them; anti-IgE and Bruton tyrosine kinase inhibitors play a more direct role in mast cell biology through abrogation of FcεRI signaling activity, whereas anti-interleukins and anti-Siglec-8 have a role in adaptive immunity modulation. CONCLUSION: In silico CSU models reproduced known CSU and therapeutic strategies features. Our results could help advance understanding of therapeutic mechanisms of action and further advance treatment research by patient profile.

Rethinking underutilized cereal crops: pan-omics integration and green system biology.

MAIN CONCLUSION: Due to harsh lifestyle changes, in the present era, nutritional security is needed along with food security so it is necessary to include underutilized cereal crops (UCCs) in our daily diet to counteract the rising danger of human metabolic illness. We can attain both the goal of zero hunger and nutritional security by developing improved UCCs using advanced pan-omics (genomics, transcriptomics, proteomics, metabolomics, nutrigenomics, phenomics and ionomics) practices. Plant sciences research progressed profoundly since the last few decades with the introduction of advanced technologies and approaches, addressing issues of food demand of the growing population, nutritional security challenges and climate change. However, throughout the expansion and popularization of commonly consumed major cereal crops such as wheat and rice, other cereal crops such as millet, rye, sorghum, and others were impeded, despite their potential medicinal and nutraceutical qualities. Undoubtedly neglected underutilized cereal crops (UCCs) also have the capability to withstand diverse climate change. To relieve the burden of major crops, it is necessary to introduce the new crops in our diet in the way of UCCs. Introgression of agronomically and nutritionally important traits by pan-omics approaches in UCCs could be a defining moment for the population's well-being on the globe. This review discusses the importance of underutilized cereal crops, as well as the application of contemporary omics techniques and advanced bioinformatics tools that could open up new avenues for future study and be valuable assets in the development and usage of UCCs in the perspective of green system biology. The increased and improved use of UCCs is dependent on number of factors that necessitate a concerted research effort in agricultural sciences. The emergence of functional genomics with molecular genetics might gear toward the reawakening of interest in underutilized cereals crops. The need of this era is to focus on potential UCCs in advanced agriculture and breeding programmes. Hence, targeting the UCCs, might provide a bright future for better health and scientific rationale for its use.

RNA2HLA: HLA-based quality control of RNA-seq datasets.

RNA-sequencing (RNA-seq) is a widely used approach for accessing the transcriptome in biomedical research. Studies frequently include multiple samples taken from the same individual at various time points or under different conditions, correct assignment of those samples to each particular participant is evidently of great importance. Here, we propose taking advantage of typing the highly polymorphic genes from the human leukocyte antigen (HLA) complex in order to verify the correct allocation of RNA-seq samples to individuals. We introduce RNA2HLA, a novel quality control (QC) tool for performing study-wide HLA-typing for RNA-seq data and thereby identifying the samples from the common source. RNA2HLA allows precise allocation and grouping of RNA samples based on their HLA types. Strikingly, RNA2HLA revealed wrongly assigned samples from publicly available datasets and thereby demonstrated the importance of this tool for the quality control of RNA-seq studies. In addition, our tool successfully extracts HLA alleles in four-digital resolution and can be used to perform massive HLA-typing from RNA-seq based studies, which will serve multiple research purposes beyond sample QC.

Extracellular matrix-induced signaling pathways in mesenchymal stem/stromal cells.

The extracellular matrix (ECM) is a crucial component of the stem cell microenvironment, or stem-cell niches, and contributes to the regulation of cell behavior and fate. Accumulating evidence indicates that different types of stem cells possess a large variety of molecules responsible for interactions with the ECM, mediating specific epigenetic rearrangements and corresponding changes in transcriptome profile. Signals from the ECM are crucial at all stages of ontogenesis, including embryonic and postnatal development, as well as tissue renewal and repair. The ECM could regulate stem cell transition from a quiescent state to readiness to perceive the signals of differentiation induction (competence) and the transition between different stages of differentiation (commitment). Currently, to unveil the complex networks of cellular signaling from the ECM, multiple approaches including screening methods, the analysis of the cell matrixome, and the creation of predictive networks of protein-protein interactions based on experimental data are used. In this review, we consider the existing evidence regarded the contribution of ECM-induced intracellular signaling pathways into the regulation of stem cell differentiation focusing on mesenchymal stem/stromal cells (MSCs) as well-studied type of postnatal stem cells totally depended on signals from ECM. Furthermore, we propose a system biology-based approach for the prediction of ECM-mediated signal transduction pathways in target cells. Video Abstract.

In vitro- and in vivo-produced male dairy calves show molecular differences in the hepatic and muscular energy regulation†.

In cattle, the in vitro production (IVP) of embryos is becoming more relevant than embryos produced in vivo, i.e. after multiple ovulation and embryo transfer (MOET). However, the effects of IVP on the developmental programming of specific organs in the postnatal calves are yet unknown. Previously, we reported an epigenomic and transcriptomic profile of the hypothalamus-pituitary-testicular axis compatible with its earlier activation in IVP calves compared to MOET animals. Here, we studied the hepatic and muscular epigenome and transcriptome of those same male dairy calves (n = 4 per group). Tissue samples from liver and semitendinosus muscle were obtained at 3 months of age, and the extracted gDNA and RNA were sequenced through whole-genome bisulfite sequencing and RNA-sequencing, respectively. Next, bioinformatic analyses determined differentially methylated cytosines or differentially expressed genes [false discovery rate (FDR) < 0.05] for each Omic dataset; and nonparametrically combined genes (NPCG) for both integrated omics (P < 0.05). KEGG pathways enrichment analysis showed that NPCG upregulated in the liver and the muscle of the IVP calves were involved in oxidative phosphorylation and the tricarboxylic acid cycle. In contrast, ribosome and translation were upregulated in the liver but downregulated in the muscle of the IVP calves compared to the MOET calves (FDR < 0.05). A model considering the effect of the methylation levels and the group on the expression of all the genes involved in these pathways confirmed these findings. In conclusion, the multiomics data integration approach indicated an altered hepatic and muscular energy regulation in phenotypically normal IVP calves compared to MOET calves.

Epigenomic and transcriptomic analyses reveal early activation of the HPG axis in in vitro-produced male dairy calves.

In cattle, several calves born after IVP ("in vitro" embryo production) present similar birthweight to those generated after MOET (multiple ovulation and embryo transfer). However, the underlying molecular patterns in organs involved in the developmental process are unknown and could indicate physiological programming. The objectives of this study were: (1) to compare epigenomic and transcriptomic modifications in the hypothalamus, pituitary, gonadal and adrenal organs between 3 months old ovum pick-up-IVP and MOET male calves (n = 4 per group) and (2) to use blood epigenomic data to proxy methylation of the inner organs. Extracted gDNA and RNA were sequenced through whole-genome bisulfite sequencing and RNA sequencing, respectively. Next, bioinformatic analyses determined differentially methylated cytosines (DMC) and differentially expressed genes (DEG) (FDR < 0.05) in IVP versus MOET samples and the KEGG pathways that were overrepresented by genes associated with DMC or DEG (FDR < 0.1). Pathways related to hypothalamus, pituitary, gonadal (HPG) axis activation (GnRH secretion in the hypothalamus, GnRH signaling in the pituitary, and steroidogenesis in the testicle) were enriched in IVP calves. Modeling the effect of the methylation levels and the group on the expression of all the genes involved in these pathways confirmed their upregulation in HPG organs in IVP calves. The application of the DIABLO method allowed the identification of 15 epigenetic and five transcriptomic biomarkers, which were able to predict the embryo origin using the epigenomic data from the blood. In conclusion, the use of an integrated epigenomic-transcriptomic approach suggested an early activation of the HPG axis in male IVP calves compared to MOET counterparts, and the identification of potential biomarkers allowed the use of blood samples to proxy methylation levels of the relevant internal organs.

Comparative Host-Pathogen Interaction Analyses of SARS-CoV2 and Aspergillus fumigatus, and Pathogenesis of COVID-19-Associated Aspergillosis.

COVID-19 caused a global catastrophe with a large number of cases making it one of the major pandemics of the human history. The clinical presentations of the disease are continuously challenging healthcare workers with the variation of pandemic waves and viral variants. Recently, SARS-CoV2 patients have shown increased occurrence of invasive pulmonary aspergillosis infection even in the absence of traditional risk factors. The mechanism of COVID-19-associated aspergillosis is not completely understood and therefore, we performed this system biological study in order to identify mechanistic implications of aspergillosis susceptibility in COVID-19 patients and the important targets associated with this disease. We performed host-pathogen interaction (HPI) analysis of SARS-CoV2, and most common COVID-19-associated aspergillosis pathogen, Aspergillus fumigatus, using in silico approaches. The known host-pathogen interactions data of SARS-CoV2 was obtained from BIOGRID database. In addition, A. fumigatus host-pathogen interactions were predicted through homology modeling. The human targets interacting with both pathogens were separately analyzed for their involvement in aspergillosis. The aspergillosis human targets were screened from DisGeNet and GeneCards. The aspergillosis targets involved in both HPI were further analyzed for functional overrepresentation analysis using PANTHER. The results indicate that both pathogens interact with a number of aspergillosis targets and altogether they recruit more aspergillosis targets in host-pathogen interaction than alone. Common aspergillosis targets involved in HPI with both SARS-CoV2 and A. fumigatus can indicate strategies for the management of both conditions by modulating these common disease targets.

Primary metabolic processes as drivers of leaf ageing.

Ageing in plants is a highly coordinated and complex process that starts with the birth of the plant or plant organ and ends with its death. A vivid manifestation of the final stage of leaf ageing is exemplified by the autumn colours of deciduous trees. Over the past decades, technological advances have allowed plant ageing to be studied on a systems biology level, by means of multi-omics approaches. Here, we review some of these studies and argue that these provide strong support for basic metabolic processes as drivers for ageing. In particular, core cellular processes that control the metabolism of chlorophyll, amino acids, sugars, DNA and reactive oxygen species correlate with leaf ageing. However, while multi-omics studies excel at identifying correlative processes and pathways, molecular genetic approaches can provide proof that such processes and pathways control ageing, by means of knock-out and ectopic expression of predicted regulatory genes. Therefore, we also review historic and current molecular evidence to directly test the hypotheses unveiled by the systems biology approaches. We found that the molecular genetic approaches, by and large, confirm the multi-omics-derived hypotheses with notable exceptions, where there is scant evidence that chlorophyll and DNA metabolism are important drivers of leaf ageing. We present a model that summarises the core cellular processes that drive leaf ageing and propose that developmental processes are tightly linked to primary metabolism to inevitably lead to ageing and death.

Anti-adipogenic signals at the onset of obesity-related inflammation in white adipose tissue.

Chronic inflammation that affects primarily metabolic organs, such as white adipose tissue (WAT), is considered as a major cause of human obesity-associated co-morbidities. However, the molecular mechanisms initiating this inflammation in WAT are poorly understood. By combining transcriptomics, ChIP-seq and modeling approaches, we studied the global early and late responses to a high-fat diet (HFD) in visceral (vWAT) and subcutaneous (scWAT) AT, the first being more prone to obesity-induced inflammation. HFD rapidly triggers proliferation of adipocyte precursors within vWAT. However, concomitant antiadipogenic signals limit vWAT hyperplastic expansion by interfering with the differentiation of proliferating adipocyte precursors. Conversely, in scWAT, residing beige adipocytes lose their oxidizing properties and allow storage of excessive fatty acids. This phase is followed by tissue hyperplastic growth and increased angiogenic signals, which further enable scWAT expansion without generating inflammation. Our data indicate that scWAT and vWAT differential ability to modulate adipocyte number and differentiation in response to obesogenic stimuli has a crucial impact on the different susceptibility to obesity-related inflammation of these adipose tissue depots.

Bioinformatics analysis of microarray data to identify hub genes, as diagnostic biomarker of HELLP syndrome: System biology approach.

BACKGROUND: HELLP syndrome is one of the disorders characterized by hemolysis, increased liver enzymes and decreased platelet count. So far, many molecular pathways and genes have been identified in relation to the pathogenesis of this syndrome; however, the main cause of the incidence and progression of the disease has not been identified. Using the biological system approach is a way to target patients by identifying genes and molecular pathways. In this study, we investigated genes and important molecular factors in the pathogenesis of HELLP syndrome. MATERIAL AND METHODS: In this study, the microarray dataset was downloaded from Gene Expression Omnibus (GEO) database and analyzed using the GEO2R online tool for identifying differentially expressed genes (DEGs). Enrichment analysis of DEGs was evaluated using the Enrichr database. Then, protein-protein interaction (PPI) networks were constructed via the STRING database; they were visualized by Cytoscape. Then the STRING database was used to construct PPI networks. The hub genes were recognized using the cytoHubba. Ultimately, the interaction of the miRNA-hub genes and drug-hub genes were also evaluated. RESULT: After analysis, it was found that some genes with the highest degree of connectivity are involved in the pathogenesis of HELLP syndrome, which are known as the hub genes. These genes are as follows: KIT, JAK2, LEP, EP300, HIST1H4L, HIST1H4F, HIST1H4H, MMP9, THBS2, and ADAMTS3. Has-miR-34a-5p was also most associated with hub genes. CONCLUSION: Finally, it can be said, that the identification of genes and molecular pathways in HELLP syndrome can be helpful in identifying the pathogenesis pathways of the disease, and designing therapeutic targets.

Genomic integration to identify molecular biomarkers associated with indicator traits of gastrointestinal nematode resistance in sheep.

This study aimed to integrate GWAS and structural variants to propose possible molecular biomarkers related to gastrointestinal nematode resistance traits in Santa Inês sheep. The phenotypic records FAMACHA, haematocrit, white blood cell count, red blood cell count, haemoglobin, platelets and egg counts per gram of faeces were collected from 700 naturally infected animals, belonging to four Brazilian flocks. A total of 576 animals were genotyped using the Ovine SNP12k BeadChip and were imputed using a reference population with Ovine SNP50 BeadChip. The GWAS approaches were based on SNPs, haplotypes, CNVs and ROH. The overlapping between the significant genomic regions detected from all approaches was investigated, and the results were integrated using a network analysis. Genes related to the immune system were found, such as ABCB1, IL6, WNT5A and IRF5. Genomic regions containing candidate genes and metabolic pathways involved in immune responses, inflammatory processes and immune cells affecting parasite resistance traits were identified. The genomic regions, biological processes and candidate genes uncovered could lead to biomarkers for selecting more resilient sheep and improving herd welfare and productivity. The results obtained are the start point to identify molecular biomarkers related to indicator traits of gastrointestinal nematode resistance in Santa Inês sheep.

Impact of Air Pollution in Airway Diseases: Role of the Epithelial Cells (Cell Models and Biomarkers).

Biomedical research is multidisciplinary and often uses integrated approaches performing different experimental models with complementary functions. This approach is important to understand the pathogenetic mechanisms concerning the effects of environmental pollution on human health. The biological activity of the substances is investigated at least to three levels using molecular, cellular, and human tissue models. Each of these is able to give specific answers to experimental problems. A scientific approach, using biological methods (wet lab), cell cultures (cell lines or primary), isolated organs (three-dimensional cell cultures of primary epithelial cells), and animal organisms, including the human body, aimed to understand the effects of air pollution on the onset of diseases of the respiratory system. Biological methods are divided into three complementary models: in vitro, ex vivo, and in vivo. In vitro experiments do not require the use of whole organisms (in vivo study), while ex vivo experiments use isolated organs or parts of organs. The concept of complementarity and the informatic support are useful tools to organize, analyze, and interpret experimental data, with the aim of discussing scientific notions with objectivity and rationality in biology and medicine. In this scenario, the integrated and complementary use of different experimental models is important to obtain useful and global information that allows us to identify the effect of inhaled pollutants on the incidence of respiratory diseases in the exposed population. In this review, we focused our attention on the impact of air pollution in airway diseases with a rapid and descriptive analysis on the role of epithelium and on the experimental cell models useful to study the effect of toxicants on epithelial cells.

Deciphering the Basis of Molecular Biology of Selected Cardiovascular Diseases: A View on Network Medicine.

Cardiovascular diseases are the leading cause of death across the world. For decades, researchers have been studying the causes of cardiovascular disease, yet many of them remain undiscovered or poorly understood. Network medicine is a recently expanding, integrative field that attempts to elucidate this issue by conceiving of disease as the result of disruptive links between multiple interconnected biological components. Still in its nascent stages, this revolutionary application of network science facilitated a number of important discoveries in complex disease mechanisms. As methodologies become more advanced, network medicine harbors the potential to expound on the molecular and genetic complexities of disease to differentiate how these intricacies govern disease manifestations, prognosis, and therapy. This is of paramount importance for confronting the incredible challenges of current and future cardiovascular disease research. In this review, we summarize the principal molecular and genetic mechanisms of common cardiac pathophysiologies as well as discuss the existing knowledge on therapeutic strategies to prevent, halt, or reverse these pathologies.

Implication of microRNAs in Carcinogenesis with Emphasis on Hematological Malignancies and Clinical Translation.

MicroRNAs (miRNAs) are evolutionarily conserved small non-coding RNAs, that are involved in the multistep process of carcinogenesis, contributing to all established hallmarks of cancer. In this review, implications of miRNAs in hematological malignancies and their clinical utilization fields are discussed. As components of the complex regulatory network of gene expression, influenced by the tissue microenvironment and epigenetic modifiers, miRNAs are "micromanagers" of all physiological processes including the regulation of hematopoiesis and metabolic pathways. Dysregulated miRNA expression levels contribute to both the initiation and progression of acute leukemias, the metabolic reprogramming of malignantly transformed hematopoietic precursors, and to the development of chemoresistance. Since they are highly stable and can be easily quantified in body fluids and tissue specimens, miRNAs are promising biomarkers for the early detection of hematological malignancies. Besides novel opportunities for differential diagnosis, miRNAs can contribute to advanced chemoresistance prediction and prognostic stratification of acute leukemias. Synthetic oligonucleotides and delivery vehicles aim the therapeutic modulation of miRNA expression levels. However, major challenges such as efficient delivery to specific locations, differences of miRNA expression patterns between pediatric and adult hematological malignancies, and potential side effects of miRNA-based therapies should be considered.