Network Theoretical Approach to Describe Epileptic Processes

Epilepsy is characterized by recurrent unprovoked seizures. Recent studies suggest that seizure generation may be caused by the abnormal activity of the entire network. This new paradigm requires new tools and methods for its study. In this sense, synchronization by linear as well as nonlinear measures are used to determine network structure and functional connectivity of neurophysiological data. Electroencephalography (EEG) data can be analyzed using each electrode’s activity as a node of the underlying cortical network. The information provided by the synchronization matrix is the basic brick upon which several lines of analysis can be performed thereafter. Detection of community structures, identification of centrality nodes, transformation of the underlying network into a simpler one, and the identification of the basic network architecture are only some of the many lines of basic works that can be done in order to characterize the epilepsy as a network disease. This chapter describes new approaches in network epilepsy, provides mathematical concepts in order to understand the complex network analyses, and reviews the advances in network analyses and its application to epilepsy research

Potential EEG biomarkers of sedation doses in intensive care patients unveiled by using a machine learning approach

Objective. Sedation of neurocritically ill patients is one of the most challenging situation in ICUs. Quantitative knowledge on the sedation effect on brain activity in that complex scenario could help to uncover new markers for sedation assessment. Hence, we aim to evaluate the existence of changes of diverse EEG-derived measures in deeply-sedated (RASS-Richmond agitation-sedation scale -4 and -5) neurocritically ill patients, and also whether sedation doses are related with those eventual changes. Approach. We performed an observational prospective cohort study in the intensive care unit of the Hospital de la Princesa. Twenty-six adult patients suffered from traumatic brain injury and subarachnoid hemorrhage were included in the present study. Long-term continuous electroencephalographic (EEG) recordings (2141 h) and hourly annotated information were used to determine the relationship between intravenous sedation infusion doses and network and spectral EEG measures. To do that, two different strategies were followed: assessment of the statistical dependence between both variables using the Spearman correlation rank and by performing an automatic classification method based on a machine learning algorithm. Main results. More than 60% of patients presented a correlation greater than 0.5 in at least one of the calculated EEG measures with the sedation dose. The automatic classification method presented an accuracy of 84.3% in discriminating between different sedation doses. In both cases the nodes' degree was the most relevant measurement. Significance. The results presented here provide evidences of brain activity changes during deep sedation linked to sedation doses. Particularly, the capability of network EEG-derived measures in discriminating between different sedation doses could be the framework for the development of accurate methods for sedation levels assessment.Fil: Sanz García, Ancor. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Pérez Romero, Miriam. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Pastor, Jesús. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Sola, Rafael G.. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa. Servicio de Neurocirugia. Grupo de Epilepsia; EspañaFil: Vega Zelaya, Lorena. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Vega, Gema. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Monasterio, Fernando. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Torrecilla, Carmen. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Pulido, Paloma. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa. Servicio de Neurocirugia. Grupo de Epilepsia; EspañaFil: Ortega, Guillermo José. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Mice Lacking Thyroid Hormone Receptor β Show Enhanced Apoptosis and Delayed Liver Commitment for Proliferation after Partial Hepatectomy

This is an open-access article distributed under the terms of the Creative Commons Attribution License.[Background]: The role of thyroid hormones and their receptors (TR) during liver regeneration after partial hepatectomy (PH) was studied using genetic and pharmacologic approaches. Roles in liver regeneration have been suggested for T3, but there is no clear evidence distinguishing the contribution of increased amounts of T3 from the modulation by unoccupied TRs. [Methodology/Principal Findings]: Mice lacking TR alpha 1/TR beta or TR beta alone fully regenerated liver mass after PH, but showed delayed commitment to the initial round of hepatocyte proliferation and transient but intense apoptosis at 48h post-PH, affecting similar to 30% of the remaining hepatocytes. Pharmacologically induced hypothyroidism yielded similar results. Loss of TR activity was associated with enhanced nitrosative stress in the liver remnant, due to an increase in the activity of the nitric oxide synthase (NOS) 2 and 3, caused by a transient decrease in the concentration of asymmetric dimethylarginine (ADMA), a potent NOS inhibitor. This decrease in the ADMA levels was due to the presence of a higher activity of dimethylarginineaminohydrolase-1 (DDAH-1) in the regenerating liver of animals lacking TR alpha 1/TR beta or TR beta. DDAH-1 expression and activity was paralleled by the activity of FXR, a transcription factor involved in liver regeneration and up-regulated in the absence of TR. [Conclusions/Significance]: We report that TRs are not required for liver regeneration; however, hypothyroid mice and TR beta-or TR alpha 1/TR beta-deficient mice exhibit a delay in the restoration of liver mass, suggesting a specific role for TRb in liver regeneration. Altered regenerative responses are related with a delay in the expression of cyclins D1 and E, and the occurrence of liver apoptosis in the absence of activated TRb that can be prevented by administration of NOS inhibitors. Taken together, these results indicate that TRb contributes significantly to the rapid initial round of hepatocyte proliferation following PH, and improves the survival of the regenerating liver at later times.This work was supported by grants BFU2008-02161, BFU2007-62402, SAF2007-60511, and SAF2007-60551 from MICINN; S-BIO-0283/2006 from Comunidad de Madrid and FIS-RECAVA RD06/0014/0025 to L.B.; and PI05.0050, PI080070, and the Fundacion Mutua Madrileña to S.H. RECAVA and Ciberehd are funded by the Instituto de Salud Carlos III. R. L-F. is supported by a fellowship from Instituto de Salud Carlos III. The CNIC is supported by the Spanish Ministry of Science and Innovation and the Pro-CNIC Foundation.Peer reviewe

Raman–Mo¨ssbauer–XRD studies of selected samples from ‘‘Los Azulejos” outcrop: A possible analogue for assessing the alteration processes on Mars

The outcrop of ‘‘Los Azulejos” is visible at the interior of the Can˜adas Caldera in Tenerife Island (Spain). It exhibits a great variety of alteration processes that could be considered as terrestrial analogue for several geological processes on Mars. This outcrop is particularly interesting due to the content of clays, zeolite, iron oxides, and sulfates corresponding to a hydrothermal alteration catalogued as ‘‘Azulejos” type alteration. A detailed analysis by portable and laboratory Raman systems as well as other different techniques such as X-ray diffraction (XRD) and Mo¨ssbauer spectroscopy has been carried out (using twin-instruments from Martian lander missions: Mo¨ssbauer spectrometer MIMOS-II from the NASA-MER mission of 2001 and the XRD diffractometer from the NASA-MSL Curiosity mission of 2012). The mineral identification presents the following mineral species: magnetite, goethite, hematite, anatase, rutile, quartz, gregoryite, sulfate (thenardite and hexahydrite), diopside, feldspar, analcime, kaolinite and muscovite. Moreover, the in-situ Raman and Micro- Raman measurements have been performed in order to compare the capabilities of the portable system specially focused for the next ESA Exo-Mars mission. The mineral detection confirms the sub-aerial alteration on the surface and the hydrothermal processes by the volcanic fluid circulations in the fresh part. Therefore, the secondary more abundant mineralization acts as the color agent of the rocks. Thus, the zeolite–illite group is the responsible for the bluish coloration, as well as the feldspars and carbonates for the whitish and the iron oxide for the redish parts. The XRD system was capable to detect a minor proportion of pyroxene, which is not visible by Raman and Mo¨ssbauer spectroscopy due to the ‘‘Azulejos” alteration of the parent material on the outcrop. On the other hand, Mo¨ ssbauer spectroscopy was capable of detecting different types of iron-oxides (Fe3+/2+-oxide phases). These analyses emphasize the strength of the different techniques and the working synergy of the three different techniques together for planetary space missions

Early Pars Plana Vitrectomy for Treatment of Acute Infective Endophthalmitis

Purpose: To evaluate the efficacy and safety of early pars plana vitrectomy (PPV) for the treatment of acute infective endophthalmitis, and identify prognostic factors for better visual outcome. Design: Retrospective cohort study. Methods: Consecutive patients who underwent early PPV within 72 hours of presentation for the treatment of acute infective bacterial endophthalmitis and presented to a large tertiary referral center in New South Wales, Australia, between January 2009 and December 2013 were included. Changes in best-corrected visual acuity (VA) from baseline to 1 year were examined. Results: A total of 64 patients were included. The inciting events were cataract surgery (53%), intravitreal injection (36%), trabeculectomy (3%), and endogenous (3%). The mean VA improved from 3.1 logMAR (hand motion) at baseline to 1.02 (approximately 20/200) at 1 year, with 42% achieving final VA equal to or better than 0.477 logMAR (20/60) following early PPV. Positive prognostic factors were negative microbial cultures (P < 0.01) and etiology of post-cataract surgery (P < 0.01). In multivariable analyses adjusting for age and prognostic factors, patients with baseline VA of light perception and hand motion achieved greater visual gains than those with counting fingers, with gains of logMAR of -2.68, -2.09, and -0.85, respectively (P < 0.0001). Conclusions: Most patients who undergo early PPV experience substantial VA improvement. Negative microbial cultures and endophthalmitis after cataract surgery were associated with better final visual outcome. Patients with presenting VA of light perception or hand motion achieved higher visual gains than those with counting fingers, suggesting the possibility that early PPV may be beneficial in both groups

RANK links senescence to stemness in the mammary epithelia, delaying tumor onset but increasing tumor aggressiveness

Rank signaling enhances stemness in mouse and human mammary epithelial cells (MECs) and mediates mammary tumor initiation. Mammary tumors initiated by oncogenes or carcinogen exposure display high levels of Rank and Rank pathway inhibitors have emerged as a new strategy for breast cancer prevention and treatment. Here, we show that ectopic Rank expression in the mammary epithelia unexpectedly delays tumor onset and reduces tumor incidence in the oncogene-driven Neu and PyMT models. Mechanistically, we have found that ectopic expression of Rank or exposure to Rankl induces senescence, even in the absence of other oncogenic mutations. Rank leads to DNA damage and senescence through p16/p19. Moreover, RANK-induced senescence is essential for Rank-driven stemness, and although initially translates into delayed tumor growth, eventually promotes tumor progression and metastasis. We uncover a dual role for Rank in the mammary epithelia: Rank induces senescence and stemness, delaying tumor initiation but increasing tumor aggressiveness

Dendritic Cell‐Mediated Cross‐Priming by a Bispecific Neutralizing Antibody Boosts Cytotoxic T Cell Responses and Protects Mice against SARS‐CoV‐2

SARS-CoV-2 B.1.351 and B.1.167.2 viruses used in this study were obtained through the European Virus Archive Global (EVA-GLOBAL) project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 653316. SARS-CoV-2 B.1 (MAD6 isolate) was kindly provided by José M. Honrubia and Luis Enjuanes (CNB-CSIC, Madrid, Spain). The authors thank Centro de Investigación en Sanidad Animal (CISA)-Instituto Nacional de Investigaciones Agrarias (INIA-CSIC) (Valdeolmos, Madrid, Spain) for the BSL-3 facilities. Research in LAV laboratory was funded by the BBVA Foundation (Ayudas Fundación BBVA a Equipos de Investigación Científica SARS-CoV-2 y COVID19); the MCIN/AEI/10.13039/501100011033 (PID2020-117323RB-I00 and PDC2021-121711-I00), partially supported by the European Regional Development Fund (ERDF); the Carlos III Health Institute (ISCIII) (DTS20/00089), partially supported by the ERDF, the Spanish Association Against Cancer (AECC 19084); the CRIS Cancer Foundation (FCRISIFI-2018 and FCRIS-2021-0090), the Fundación Caixa-Health Research (HR21-00761 project IL7R_LungCan), and the Comunidad de Madrid (P2022/BMD-7225 NEXT_GEN_CART_MAD-CM). Work in the DS laboratory was funded by the CNIC; the European Union’s Horizon 2020 research and innovation program under grant agreement ERC-2016-Consolidator Grant 725091; MCIN/AEI/10.13039/501100011033 (PID2019-108157RB); Comunidad de Madrid (B2017/BMD-3733 Immunothercan-CM); Atresmedia (Constantes y Vitales prize); Fondo Solidario Juntos (Banco Santander); and “La Caixa” Foundation (LCF/PR/HR20/00075). The CNIC was supported by the ISCIII, the MCIN and the Pro CNIC Foundation and is a Severo Ochoa Center of Excellence (CEX2020- 001041-S funded by MCIN/AEI/10.13039/501100011033). Research in RD laboratory was supported by the ISCIII (PI2100989) and CIBERINFEC; the European Commission Horizon 2020 Framework Programme (grant numbers 731868 project VIRUSCAN FETPROACT-2016, and 101046084 project EPIC-CROWN-2); and the Fundación CaixaHealth Research (grant number HR18-00469 project StopEbola). Research in CNB-CSIC laboratory was funded by Fondo Supera COVID19 (Crue Universidades-Banco Santander) grant, CIBERINFEC, and Spanish Research Council (CSIC) grant 202120E079 (to J.G.-A.), CSIC grant 2020E84 (to M.E.), MCIN/AEI/10.13039/501100011033 (PID2020- 114481RB-I00 to J.G-A. and M.E.), and by the European CommissionNextGenerationEU, through CSIC’s Global Health Platform (PTI Salud Global) to J.G.-A. and M.E. Work in the CIB-CSIC laboratory was supported by MCIN/AEI/10.13039/501100011033 (PID2019-104544GB-I00 and 2023AEP105 to CA, and PID2020-113225GB-I00 to F.J.B.). Cryo-EM data were collected at the Maryland Center for Advanced Molecular Analyses which was supported by MPOWER (The University of Maryland Strategic Partnership). I.H.-M. receives the support of a fellowship from la Caixa Foundation (ID 100010434, fellowship code: LCF/BQ/IN17/11620074) and from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 71367. L.R.-P. was supported by a predoctoral fellowship from the Immunology Chair, Universidad Francisco de Vitoria/Merck.S

Spanish guidelines for the use of targeted deep sequencing in myelodysplastic syndromes and chronic myelomonocytic leukaemia

The landscape of medical sequencing has rapidly changed with the evolution of next generation sequencing (NGS). These technologies have contributed to the molecular characterization of the myelodysplastic syndromes (MDS) and chronic myelomonocytic leukaemia (CMML), through the identification of recurrent gene mutations, which are present in >80% of patients. These mutations contribute to a better classification and risk stratification of the patients. Currently, clinical laboratories include NGS genomic analyses in their routine clinical practice, in an effort to personalize the diagnosis, prognosis and treatment of MDS and CMML. NGS technologies have reduced the cost of large-scale sequencing, but there are additional challenges involving the clinical validation of these technologies, as continuous advances are constantly being made. In this context, it is of major importance to standardize the generation, analysis, clinical interpretation and reporting of NGS data. To that end, the Spanish MDS Group (GESMD) has expanded the present set of guidelines, aiming to establish common quality standards for the adequate implementation of NGS and clinical interpretation of the results, hoping that this effort will ultimately contribute to the benefit of patients with myeloid malignancies

Inferring the Regulatory Network of the miRNA-mediated Response to Biotic and Abiotic Stress in Melon

[EN] Background: MiRNAs have emerged as key regulators of stress response in plants, suggesting their potential as candidates for knock-in/out to improve stress tolerance in agricultural crops. Although diverse assays have been performed, systematic and detailed studies of miRNA expression and function during exposure to multiple environments in crops are limited. Results: Here, we present such pioneering analysis in melon plants in response to seven biotic and abiotic stress conditions. Deep-sequencing and computational approaches have identified twenty-four known miRNAs whose expression was significantly altered under at least one stress condition, observing that down-regulation was preponderant. Additionally, miRNA function was characterized by high scale degradome assays and quantitative RNA measurements over the intended target mRNAs, providing mechanistic insight. Clustering analysis provided evidence that eight miRNAs showed a broad response range under the stress conditions analyzed, whereas another eight miRNAs displayed a narrow response range. Transcription factors were predominantly targeted by stressresponsive miRNAs in melon. Furthermore, our results show that the miRNAs that are down-regulated upon stress predominantly have as targets genes that are known to participate in the stress response by the plant, whereas the miRNAs that are up-regulated control genes linked to development. Conclusion: Altogether, this high-resolution analysis of miRNA-target interactions, combining experimental and computational work, Illustrates the close interplay between miRNAs and the response to diverse environmental conditions, in melon.The authors thank Dr. A. Monforte for providing melon seeds and Dra. B. Pico (Cucurbits Group - COMAV) for providing melon seeds and Monosporascus isolate respectively. This work was supported by grants AGL2016-79825-R, BIO2014-61826-EXP (GG), and BFU2015-66894-P (GR) from the Spanish Ministry of Economy and Competitiveness (co-supported by FEDER). The funders had no role in the experiment design, data analysis, decision to publish, or preparation of the manuscript.Sanz-Carbonell, A.; Marques Romero, MC.; Bustamante-González, AJ.; Fares Riaño, MA.; Rodrigo Tarrega, G.; Gomez, GG. (2019). Inferring the Regulatory Network of the miRNA-mediated Response to Biotic and Abiotic Stress in Melon. BMC Plant Biology. 1-17. https://doi.org/10.1186/s12870-019-1679-0S117Zhang B. MicroRNAs: a new target for improving plant tolerance to abiotic stress. J Exp Bot. 2015;66:1749–61.Zhu JK. Abiotic stress signaling and responses in plants. Cell. 2016;167:313–24.Bielach A, Hrtyan M, Tognetti VB. Plants under stress: involvement of auxin and Cytokinin. Int J Mol Sci. 2017;4(18):7.Zarattini M, Forlani G. Toward unveiling the mechanisms for transcriptional regulation of proline biosynthesis in the plant cell response to biotic and abiotic stress conditions. Front Plant Sci. 2017;2(8):927.Nolan T, Chen J, Yin Y. Cross-talk of Brassinosteroid signaling in controlling growth and stress responses. Biochem J. 2017;474:2641–61.Mittler R. Abiotic stress, the field environment and stress combinations. Trends Plant Sci. 2006;11:15–9.Djami-Tchatchou AT, Sanan-Mishra N, Ntushelo K, Dubery IA. Functional roles of microRNAs in Agronomically important plants—potential as targets for crop improvement and protection. Front Plant Sci. 2017;8:378.Baxter A, Mittler R, Suzuki N. ROS as key players in plant stress signaling. J Exp Bot. 2014;65:1229–40.Golldack D, Li C, Mohan H, Probst N. Tolerance to drought and salt stress in plants: unraveling the signaling networks. Front Plant Sci. 2014;5:151.Lee SH, Li HW, Koh KW, Chuang HY, Chen YR, Lin CS, Chan MT. MSRB7 reverses oxidation of GSTF2/3 to confer tolerance of Arabidopsis thaliana to oxidative stress. J Exp Bot. 2014;65:5049–62.Carrera J, Rodrigo G, Jaramillo A, Elena SF. Reverse-engineering the Arabidopsis thaliana transcriptional network under changing environmental conditions. Genome Biol. 2009;10(9):R96.Shriram V, Kumar V, Devarumath RM, Khare TS, Wani SH. MicroRNAs as potential targets for abiotic stress tolerance in plants. Front Plant Sci. 2016;7:817.Sunkar R, Chinnusamy V, Zhu J, Zhu JH. Small RNAs as big players in plant abiotic stress responses and nutrient deprivation. Trends Plant Sci. 2007;12:301–9.Kumar R. Role of microRNAs in biotic and abiotic stress responses in crop plants. Appl Biochem Biotechnology. 2014;174:93–115.Reis RS, Eamens AL, Waterhouse PM. Missing pieces in the puzzle of plant MicroRNAs. Trends Plant Sci. 2015;20:721–8.Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004;116:281–97.Borges F, Martienssen RA. The expanding world of small RNAs in plants. Nat Rev Mol Cell Biol. 2015;16:727–41.Axtell MJ, Bartel DP. Antiquity of microRNAs and their targets in land-plants. Plant Cell. 2005;17:1658–73.Cuperus JT, Fahlgren N, Carrington JC. Evolution and functional diversification of MIRNA genes. Plant Cell. 2011;23:431–42.Cui J, You C, Chen X. The evolution of microRNAs in plants. Current Opinions in Plant Biology. 2016;35:61–7.Sunkar R, Li YF, Jagadeeswaran G. Functions of microRNAs in plant stress responses. Trends Plant Sci. 2012;17:196–203.Zhang T, Zhao YL, Zhao JH, Wang S, Jin Y, Chen ZQ, Fang YY, Hua CL, Ding SW, Guo HS. Cotton plants export microRNAs to inhibit virulence gene expression in a fungal pathogen. Nature Plants. 2016;2(10):16153.Chaloner T, vanKan JA, Grant-Downton R. RNA ‘Information Warfare’ in pathogenic and mutualistic interactions. Trends Plant Sci. 2016;9:738–48.Niu D, Wang Z, Wang S, Qiao L Zhao H. Profiling of small RNAs involved in plant-pathogen interactions. Methods Molecular Biology. 2015;1287:61–79.Wei S, Wang L, Zhang Y, Huang D. Identification of early response genes to salt stress in roots of melon (Cucumis melo L.) seedlings. Molecular Biology Report. 2013;40:2915–26.Clepet C, Joobeur T, Zheng Y, Jublot D, Huang M, Truniger V, et al. Analysis of expressed sequence tags generated from full-length enriched cDNA libraries of melon. BMC Genomics. 2011;12:252.González M, Xu M, Esteras C, Roig C, Monforte AJ, Troadec C, et al. Towards a TILLING platform for functional genomics in Piel de Sapo melons. BMC Research Notes. 2011;4:289.García MJ. The genome of melon (Cucumis melo L.). Proc Natl Acad Sci U S A. 2012;109:11872–7.Pollack FG, Uecker FA. Monosporascus cannonballus: an unusual ascomycete in cantaloupe roots. Mycologia. 1974;66:346–9.Kofalvi S, Marcos J, Cañizares MC, Pallas V, Candresse T. Hop stunt viroid (HSVd) sequence variants from Prunus species: evidence for recombination between HSVd isolates. J Gen Virol. 1997;78:3177–86.Sattar S, Song Y, Anstead J, Sunkar R, Thompson G. Cucumis melo expression profile during aphid herbivory in a resistant and susceptible interaction. Mol Plant-Microbe Interact. 2012;25:839–48.Herranz MC, Navarro JA, Sommen E, Pallas V. Comparative analysis among the small RNA populations of source, sink and conductive tissues in two different plant-virus pathosystems. BMC Genomics. 2015;16:117.Jagadeeswaran G, Nimmakayala P, Zheng Y, Gowdu K, Reddy UK, Sunkar R. Characterization of the small RNA component of leaves and fruits from four different cucurbit species. BMC Genomics. 2012;13:329.Kozomara A, Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2014;42:D68–73.Barciszewska-Pacak M, Milanowska K, Knop K, Bielewicz D, Nuc P, Plewka P, et al. Arabidopsis microRNA expression regulation in a wide range of abiotic stress responses. Front Plant Sci. 2015;6:410.Zhou L, Liu Y, Liu Z, Kong D, Duan M, Luo L. Genome-wide identification and analysis of drought-responsive microRNAs in Oryza sativa. J Exp Bot. 2010;61:4157–68.Samad A, Sajad M, Nazaruddin N, Fauzi I, Murad A, Zainal Z, Ismanizan Ismail I. MicroRNA and transcription factor: key players in plant regulatory network. Front Plant Sci. 2017;8:565.Danisman S. TCP transcription factors at the Interface between environmental challenges and the Plant’s growth responses. Front Plant Sci. 2016;7:1930.Llave C, Xie Z, Kasschau KD, Carrington JC. Cleavage of scarecrow-like mRNA targets directed by a class of Arabidopsis miRNA. Science. 2002;297:2053–6.Gupta OP, Meena NL, Sharma I, et al. Differential regulation of microRNAs in response to osmotic, salt and cold stresses in wheat. Mol Biol Rep. 2014;41:4623.Wang M, Wang Q, Zhang B. 2013. Response of miRNAs and their targets to salt and drought stresses in cotton (Gossypium hirsutum ). Gene 30: 26–32.Savageau MA. Demand theory of gene regulation. I. Quantitative development of the theory. Genetics. 1998;149:1665–76.Negrão S, Schmöckel SM, Tester M. Evaluating physiological responses of plants to salinity stress. Ann Bot. 2017;119:1–11.Barabasi AL, Oltvai ZN. Network biology: understanding the cell's functional organization. Nat Rev Genet. 2004;5(2):101–13.Megraw M, Cumbie J, Ivanchenko M, Filichkin S. Small genetic circuits and MicroRNAs: big players in polymerase II transcriptional control in plants. Plant Cell. 2016;28:286–303.Wang St, Sun Xl, Hoshino Y, Yu Y, Jia B, et al. 2014. MicroRNA319 Positively Regulates Cold Tolerance by Targeting OsPCF6 and OsTCP21 in Rice (Oryza sativa). PLoS ONE 9(3): e91357.Fang Y, Xie K, Xiong L. Conserved miR164-targeted NAC genes regulate drought resistence in rice. J Exp Bot. 2014;65:2119–35.Goossens A, de la Fuente N, Forment J, Serrano R, Portillo F. Regulation of yeast H+-ATPase by protein kinases belonging to a family dedicated to activation of plasma membrane transporters. Mol Cell Biol. 2000;20:7654–61.Roig C, Fita A, Ríos G, Hammond JP, Nuez F, Picó B. Root transcriptional responses of two melon genotypes with contrasting resistance to Monosporascus cannonballus (Pollack et Uecker) infection. BMC Genomics. 2012;13:601.Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet Journal. 2011;17:10–2.R Core Team 2013. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3–900051–07-0, URL http://www.R-project.org /.Tarazona S, Furió-Tarí P, Turrà D, Di Pietro A, Nueda MJ, Ferrer A, Conesa A. Data quality aware analysis of differential expression in RNA-seq with NOISeq R/bioc package. Nucleic Acids Res. 2015;43:e140.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.Czimmerer Z, Hulvely J, Simandi Z, Varallyay E, Havelda Z, Szabo E, Balint BL. A versatile method to design stem-loop primer-based quantitative PCR assays for detecting small regulatory RNA molecules. PLoS One. 2013;8(1):e55168.Zhai J, Arikit S, Simon S, Kingham B, Meyers B. Rapid construction of parallel analysis of RNA end (PARE) libraries for Illumina sequencing. Methods. 2014;67:84–90.Pink S, Vogel S. 2014. D3NETWORK: Stata module to create network visualizations using D3.js http://EconPapers.repec.org/RePEc:boc:bocode:s457844 .Csardi G, Nepusz T. The igraph software package for complex network research. Int J Complex Systems. 2006;1695:1–9