AI-Enhanced Analysis Reveals Impact of Maternal Diabetes on Subcutaneous Fat Mass in Fetuses without Growth Alterations.

Pregnant women with diabetes often present impaired fetal growth, which is less common if maternal diabetes is well-controlled. However, developing strategies to estimate fetal body composition beyond fetal growth that could better predict metabolic complications later in life is essential. This study aimed to evaluate subcutaneous fat tissue (femur and humerus) in fetuses with normal growth among pregnant women with well-controlled diabetes using a reproducible 3D-ultrasound tool and offline TUI (Tomographic Ultrasound Imaging) analysis. Additionally, three artificial intelligence classifier models were trained and validated to assess the clinical utility of the fetal subcutaneous fat measurement. A significantly larger subcutaneous fat area was found in three-femur and two-humerus selected segments of fetuses from women with diabetes compared to the healthy pregnant control group. The full classifier model that includes subcutaneous fat measure, gestational age, fetal weight, fetal abdominal circumference, maternal body mass index, and fetal weight percentile as variables, showed the best performance, with a detection rate of 70%, considering a false positive rate of 10%, and a positive predictive value of 82%. These findings provide valuable insights into the impact of maternal diabetes on fetal subcutaneous fat tissue as a variable independent of fetal growth.

Role of Moonlighting Proteins in Disease: Analyzing the Contribution of Canonical and Moonlighting Functions in Disease Progression.

The term moonlighting proteins refers to those proteins that present alternative functions performed by a single polypeptide chain acquired throughout evolution (called canonical and moonlighting, respectively). Over 78% of moonlighting proteins are involved in human diseases, 48% are targeted by current drugs, and over 25% of them are involved in the virulence of pathogenic microorganisms. These facts encouraged us to study the link between the functions of moonlighting proteins and disease. We found a large number of moonlighting functions activated by pathological conditions that are highly involved in disease development and progression. The factors that activate some moonlighting functions take place only in pathological conditions, such as specific cellular translocations or changes in protein structure. Some moonlighting functions are involved in disease promotion while others are involved in curbing it. The disease-impairing moonlighting functions attempt to restore the homeostasis, or to reduce the damage linked to the imbalance caused by the disease. The disease-promoting moonlighting functions primarily involve the immune system, mesenchyme cross-talk, or excessive tissue proliferation. We often find moonlighting functions linked to the canonical function in a pathological context. Moonlighting functions are especially coordinated in inflammation and cancer. Wound healing and epithelial to mesenchymal transition are very representative. They involve multiple moonlighting proteins with a different role in each phase of the process, contributing to the current-phase phenotype or promoting a phase switch, mitigating the damage or intensifying the remodeling. All of this implies a new level of complexity in the study of pathology genesis, progression, and treatment. The specific protein function involved in a patient's progress or that is affected by a drug must be elucidated for the correct treatment of diseases.

Multimodal Early Birth Weight Prediction Using Multiple Kernel Learning.

In this work, a novel multimodal learning approach for early prediction of birth weight is presented. Fetal weight is one of the most relevant indicators in the assessment of fetal health status. The aim is to predict early birth weight using multimodal maternal-fetal variables from the first trimester of gestation (Anthropometric data, as well as metrics obtained from Fetal Biometry, Doppler and Maternal Ultrasound). The proposed methodology starts with the optimal selection of a subset of multimodal features using an ensemble-based approach of feature selectors. Subsequently, the selected variables feed the nonparametric Multiple Kernel Learning regression algorithm. At this stage, a set of kernels is selected and weighted to maximize performance in birth weight prediction. The proposed methodology is validated and compared with other computational learning algorithms reported in the state of the art. The obtained results (absolute error of 234 g) suggest that the proposed methodology can be useful as a tool for the early evaluation and monitoring of fetal health status through indicators such as birth weight.

Inflammatory-Metal Profile as a Hallmark for COVID-19 Severity During Pregnancy.

Pregnancy makes women more susceptible to infectious agents; however, available data on the effect of SARS-CoV-2 on pregnant women are limited. To date, inflammatory responses and changes in serum metal concentration have been reported in COVID-19 patients, but few associations between metal ions and cytokines have been described. The aim of this study was to evaluate correlations between inflammatory markers and serum metal ions in third-trimester pregnant women with varying COVID-19 disease severity. Patients with severe symptoms had increased concentrations of serum magnesium, copper, and calcium ions and decreased concentrations of iron, zinc, and sodium ions. Potassium ions were unaffected. Pro-inflammatory cytokines IL-6, TNF-α, IL-8, IL-1α, anti-inflammatory cytokine IL-4, and the IP-10 chemokine were induced in the severe presentation of COVID-19 during pregnancy. Robust negative correlations between iron/magnesium and zinc/IL-6, and a positive correlation between copper/IP-10 were observed in pregnant women with the severe form of the disease. Thus, coordinated alterations of serum metal ions and inflammatory markers - suggestive of underlying pathophysiological interactions-occur during SARS-CoV-2 infection in pregnancy.

Genotoxicity Associated with Residual Monomers in Restorative Dentistry: A Systematic Review.

PURPOSE: Incomplete polymerisation processes produce several leachable substances. The aim of this work was to review, through existing research and published literature, the genotoxic effect of residual monomers of polymers used in restorative dentistry. MATERIALS AND METHODS: The selection of published studies was performed on six databases from January 2000 to June 2020. The keywords used were: 'genotoxicity' or 'DNA damage' and 'dental resin' or 'methacrylates' or 'residual monomers'. The selection was carried out according to the parameters and guidelines of the Preferred Reporting Items for Systematic Review and Metanalyses (PRISMA) and was based on patient, intervention, comparison, and outcome (PICO). The inclusion criteria were: in vitro and in vivo studies published in English that evaluated genotoxicity for residual monomers leached from polymers related to restorative dentistry. Case reports and review articles were excluded. RESULTS: Twenty-seven studies met the eligibility criteria. Two categories were constructed based on the experimental design, in vivo and in vitro reports. For the in vitro research, two main methods of assessing DNA damage were reported in selected studies: micronucleus (MN) counting and alkaline comet assay. For in vivo reports, the main method for assessing genotoxic damage was MN counting. CONCLUSION: From the electronic search, structured data extraction, and analysis by different independent reviewers, results from the present systematic review allow us to conclude that DNA damage is induced by monomers/co-monomers (triethylene glycol dimethacrylate, bisphenol-A-glycidyl methacrylate, urethane dimethacrylate, and 2-hydroxyethyl methacrylate) that are used in restorative dentistry. This systematic review highlights the need for more research on the use of monomers/co-monomers to properly assess clinical biocompatibility.

Secondary resistance to anti-EGFR therapy by transcriptional reprogramming in patient-derived colorectal cancer models.

BACKGROUND: The development of secondary resistance (SR) in metastatic colorectal cancer (mCRC) treated with anti-epidermal growth factor receptor (anti-EGFR) antibodies is not fully understood at the molecular level. Here we tested in vivo selection of anti-EGFR SR tumors in CRC patient-derived xenograft (PDX) models as a strategy for a molecular dissection of SR mechanisms. METHODS: We analyzed 21 KRAS, NRAS, BRAF, and PI3K wildtype CRC patient-derived xenograft (PDX) models for their anti-EGFR sensitivity. Furthermore, 31 anti-EGFR SR tumors were generated via chronic in vivo treatment with cetuximab. A multi-omics approach was employed to address molecular primary and secondary resistance mechanisms. Gene set enrichment analyses were used to uncover SR pathways. Targeted therapy of SR PDX models was applied to validate selected SR pathways. RESULTS: In vivo anti-EGFR SR could be established with high efficiency. Chronic anti-EGFR treatment of CRC PDX tumors induced parallel evolution of multiple resistant lesions with independent molecular SR mechanisms. Mutations in driver genes explained SR development in a subgroup of CRC PDX models, only. Transcriptional reprogramming inducing anti-EGFR SR was discovered as a common mechanism in CRC PDX models frequently leading to RAS signaling pathway activation. We identified cAMP and STAT3 signaling activation, as well as paracrine and autocrine signaling via growth factors as novel anti-EGFR secondary resistance mechanisms. Secondary resistant xenograft tumors could successfully be treated by addressing identified transcriptional changes by tailored targeted therapies. CONCLUSIONS: Our study demonstrates that SR PDX tumors provide a unique platform to study molecular SR mechanisms and allow testing of multiple treatments for efficient targeting of SR mechanisms, not possible in the patient. Importantly, it suggests that the development of anti-EGFR tolerant cells via transcriptional reprogramming as a cause of anti-EGFR SR in CRC is likely more prevalent than previously anticipated. It emphasizes the need for analyses of SR tumor tissues at a multi-omics level for a comprehensive molecular understanding of anti-EGFR SR in CRC.

Degradation of CCNK/CDK12 is a druggable vulnerability of colorectal cancer.

Novel treatment options for metastatic colorectal cancer (CRC) are urgently needed to improve patient outcome. Here, we screen a library of non-characterized small molecules against a heterogeneous collection of patient-derived CRC spheroids. By prioritizing compounds with inhibitory activity in a subset of-but not all-spheroid cultures, NCT02 is identified as a candidate with minimal risk of non-specific toxicity. Mechanistically, we show that NCT02 acts as molecular glue that induces ubiquitination of cyclin K (CCNK) and proteasomal degradation of CCNK and its complex partner CDK12. Knockout of CCNK or CDK12 decreases proliferation of CRC cells in vitro and tumor growth in vivo. Interestingly, sensitivity to pharmacological CCNK/CDK12 degradation is associated with TP53 deficiency and consensus molecular subtype 4 in vitro and in patient-derived xenografts. We thus demonstrate the efficacy of targeted CCNK/CDK12 degradation for a CRC subset, highlighting the potential of drug-induced proteolysis for difficult-to-treat types of cancer.

Functional States in Tumor-Initiating Cell Differentiation in Human Colorectal Cancer.

Intra-tumor heterogeneity of tumor-initiating cell (TIC) activity drives colorectal cancer (CRC) progression and therapy resistance. Here, we used single-cell RNA-sequencing of patient-derived CRC models to decipher distinct cell subpopulations based on their transcriptional profiles. Cell type-specific expression modules of stem-like, transit amplifying-like, and differentiated CRC cells resemble differentiation states of normal intestinal epithelial cells. Strikingly, identified subpopulations differ in proliferative activity and metabolic state. In summary, we here show at single-cell resolution that transcriptional heterogeneity identifies functional states during TIC differentiation. Furthermore, identified expression signatures are linked to patient prognosis. Targeting transcriptional states associated to cancer cell differentiation might unravel novel vulnerabilities in human CRC.

Intensive Medical Nutrition Therapy Alone or with Added Metformin to Prevent Gestational Diabetes Mellitus among High-Risk Mexican Women: A Randomized Clinical Trial.

The aim of this study was to examine the efficacy of intensive medical nutrition therapy (MNT) plus metformin in preventing gestational diabetes mellitus (GDM) among high-risk Mexican women. An open-label randomized clinical trial was conducted. Inclusion criteria were pregnant women with three or more GDM risk factors: Latino ethnic group, maternal age >35 years, body mass index >25 kg/m2, insulin resistance, and a history of previous GDM, prediabetes, a macrosomic neonate, polycystic ovarian syndrome, or a first-degree relative with type 2 diabetes. Women before 15 weeks of gestation were assigned to group 1 (n = 45): intensive MNT-plus metformin (850 mg twice/day) or group 2 (n = 45): intensive MNT without metformin. Intensive MNT included individual dietary counseling, with ≤50% of total energy from high carbohydrates. The primary outcome was the GDM incidence according to the International Association of Diabetes Pregnancy Study Groups criteria. There were no significant differences in baseline characteristics and adverse perinatal outcomes between the groups. The GDM incidence was n = 11 (24.4%) in the MNT plus metformin group versus n = 7 (15.5%) in the MNT without metformin group: p = 0.42 (RR: 1.57 [95% CI: 0.67-3.68]). There is no benefit in adding metformin to intensive MNT to prevent GDM among high-risk Mexican women. Clinical trials registration: NCT01675310.

Vitamin D Deficiency in Mexican Pregnant Women: Is Supplementation with ≤400 IU/day Enough?

Controversy remains surrounding vitamin D routine supplementation in healthy pregnancy, and the doses are unclear. The aim of this study was to describe maternal vitamin D status throughout pregnancy in a group of Mexican women and evaluate the effect of frequently prescribed doses of vitamin D3 on longitudinal 25-OH-D concentrations, adjusting for obesity, season, and other factors. We conducted a cohort study (Instituto Nacional de Perinatología-INPer) (2017-2020)) of healthy pregnant women without complications. Pregestational overweight/obesity (body mass index ≥ 25), vitamin D3 supplementation (prescribed by physician; 0-250, 250-400, and >400 IU/day), and serum 25-OH-D concentrations (ELISA) were evaluated in each trimester of pregnancy. Vitamin D deficiency or insufficiency was computed (<20 and <30 ng/mL, respectively). We studied 141 adult women; 58.5% had pregestational obesity or overweight. In the first trimester, 45.8% of the women were supplemented with vitamin D3; 51.4% had vitamin D insufficiency and 37.3%, deficiency. In the third trimester, 75.4% of the women were supplemented, and 20% of them still had deficiency. The final general mixed linear model showed that 25-OH-D significantly increased throughout pregnancy (p < 0.001); the highest increase was observed in the third trimester in women with doses >400 IU/day of vitamin D3 (+4 ng/mL, 95% CI: 1.72-8.11 ng/mL). In winter/autumn, 25-OH-D concentrations were also lower (p ≤ 0.05). In this group of pregnant Mexican women, the prevalence of vitamin D deficiency and insufficiency was high. A higher increase in 25-OH-D concentrations during pregnancy was observed when the women were supplemented with >400 IU/day. Common supplementation doses of 250-400 IU/day were insufficient for achieving an adequate maternal vitamin D status.

The sFlt-1/PlGF ratio as a triage tool to identify superimposed preeclampsia in women with chronic hypertension in emergency rooms.

OBJECTIVES: Assess the usefulness of the sFlt-1/PlGF ratio for the differential diagnosis of uncontrolled chronic hypertension vs. superimposed preeclampsia. STUDY DESIGN: We performed a cross-sectional study from 2015 to 2017 and 42 women with initial diagnosis of superimposed preeclampsia were enrolled in the emergency room. After a 12 week follow up patients were grouped as superimposed preeclampsia (Group A) and uncontrolled chronic hypertension (Group B) according to the American College of Obstetricians and Gynecologist criteria. A group of 33 healthy women paired by gestational age were included as controls (Group C). Maternal serum levels of sFlt-1 and PlGF were measured at enrollment, and the ratios of the groups were compared. MAIN OUTCOME MEASURES: Superimposed preeclampsia vs. uncontrolled chronic hypertension. RESULTS: After follow-up, group distribution was 30 women in Group A, 12 women in Group B, and 25 women in Group C. The sFlt-1/PlGF ratio was higher in women with superimposed preeclampsia than in women with uncontrolled chronic hypertension (215.5 vs. 9.65, p < 0.001). The control group displayed lower ratio values (3.66, p < 0.001). The sFlt-1 concentration was higher in Group A than in Group B (7564 vs. 1281 pg/mL, p < 0.001) and the PlGF level was lower in Group A (34.39 vs. 169 pg/mL, p < 0.001). CONCLUSIONS: The sFlt-1/PlGF ratio exhibits good performance for the differential diagnosis of superimposed preeclampsia vs. uncontrolled chronic hypertension.

Placental Microarray Profiling Reveals Common mRNA and lncRNA Expression Patterns in Preeclampsia and Intrauterine Growth Restriction.

Preeclampsia (PE) and Intrauterine Growth Restriction (IUGR) are major contributors to perinatal morbidity and mortality. These pregnancy disorders are associated with placental dysfunction and share similar pathophysiological features. The aim of this study was to compare the placental gene expression profiles including mRNA and lncRNAs from pregnant women from four study groups: PE, IUGR, PE-IUGR, and normal pregnancy (NP). Gene expression microarray analysis was performed on placental tissue obtained at delivery and results were validated using RTq-PCR. Differential gene expression analysis revealed that the largest transcript variation was observed in the IUGR samples compared to NP (n = 461; 314 mRNAs: 252 up-regulated and 62 down-regulated; 133 lncRNAs: 36 up-regulated and 98 down-regulated). We also detected a group of differentially expressed transcripts shared between the PE and IUGR samples compared to NP (n = 39), including 9 lncRNAs with a high correlation degree (p < 0.05). Functional enrichment of these shared transcripts showed that cytokine signaling pathways, protein modification, and regulation of JAK-STAT cascade are over-represented in both placental ischemic diseases. These findings contribute to the molecular characterization of placental ischemia showing common epigenetic regulation implicated in the pathophysiology of PE and IUGR.

Syncytiotrophoblast of Placentae from Women with Zika Virus Infection Has Altered Tight Junction Protein Expression and Increased Paracellular Permeability.

The cytotrophoblast of human placenta transitions into an outer multinucleated syncytiotrophoblast (STB) layer that covers chorionic villi which are in contact with maternal blood in the intervillous space. During pregnancy, the Zika virus (ZIKV) poses a serious prenatal threat. STB cells are resistant to ZIKV infections, yet placental cells within the mesenchyme of chorionic villi are targets of ZIKV infection. We seek to determine whether ZIKV can open the paracellular pathway of STB cells. This route is regulated by tight junctions (TJs) which are present in the uppermost portion of the lateral membranes of STB cells. We analyzed the paracellular permeability and expression of E-cadherin, occludin, JAMs -B and -C, claudins -1, -3, -4, -5 and -7, and ZO-1, and ZO-2 in the STB of placentae from ZIKV-infected and non-infected women. In ZIKV-infected placentae, the pattern of expression of TJ proteins was preserved, but the amount of claudin-4 diminished. Placentae from ZIKV-infected women were permeable to ruthenium red, and had chorionic villi with a higher mean diameter and Hofbauer hyperplasia. Finally, ZIKV added to the basolateral surface of a trophoblast cell line reduced the transepithelial electrical resistance. These results suggest that ZIKV can open the paracellular pathway of STB cells.

Multifunctional Proteins: Involvement in Human Diseases and Targets of Current Drugs.

Multifunctionality or multitasking is the capability of some proteins to execute two or more biochemical functions. The objective of this work is to explore the relationship between multifunctional proteins, human diseases and drug targeting. The analysis of the proportion of multitasking proteins from the MultitaskProtDB-II database shows that 78% of the proteins analyzed are involved in human diseases. This percentage is much higher than the 17.9% found in human proteins in general. A similar analysis using drug target databases shows that 48% of these analyzed human multitasking proteins are targets of current drugs, while only 9.8% of the human proteins present in UniProt are specified as drug targets. In almost 50% of these proteins, both the canonical and moonlighting functions are related to the molecular basis of the disease. A procedure to identify multifunctional proteins from disease databases and a method to structurally map the canonical and moonlighting functions of the protein have also been proposed here. Both of the previous percentages suggest that multitasking is not a rare phenomenon in proteins causing human diseases, and that their detailed study might explain some collateral drug effects.

Key Clinical Factors Predicting Adipokine and Oxidative Stress Marker Concentrations among Normal, Overweight and Obese Pregnant Women Using Artificial Neural Networks.

Maternal obesity has been related to adverse neonatal outcomes and fetal programming. Oxidative stress and adipokines are potential biomarkers in such pregnancies; thus, the measurement of these molecules has been considered critical. Therefore, we developed artificial neural network (ANN) models based on maternal weight status and clinical data to predict reliable maternal blood concentrations of these biomarkers at the end of pregnancy. Adipokines (adiponectin, leptin, and resistin), and DNA, lipid and protein oxidative markers (8-oxo-2'-deoxyguanosine, malondialdehyde and carbonylated proteins, respectively) were assessed in blood of normal weight, overweight and obese women in the third trimester of pregnancy. A Back-propagation algorithm was used to train ANN models with four input variables (age, pre-gestational body mass index (p-BMI), weight status and gestational age). ANN models were able to accurately predict all biomarkers with regression coefficients greater than R² = 0.945. P-BMI was the most significant variable for estimating adiponectin and carbonylated proteins concentrations (37%), while gestational age was the most relevant variable to predict resistin and malondialdehyde (34%). Age, gestational age and p-BMI had the same significance for leptin values. Finally, for 8-oxo-2'-deoxyguanosine prediction, the most significant variable was age (37%). These models become relevant to improve clinical and nutrition interventions in prenatal care.

A multimodal imaging workflow to visualize metal mixtures in the human placenta and explore colocalization with biological response markers.

Fetal exposure to essential and toxic metals can influence life-long health trajectories. The placenta regulates chemical transmission from maternal circulation to the fetus and itself exhibits a complex response to environmental stressors. The placenta can thus be a useful matrix to monitor metal exposures and stress responses in utero, but strategies to explore the biologic effects of metal mixtures in this organ are not well-developed. In this proof-of-concept study, we used laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) to measure the distributions of multiple metals in placental tissue from a low-birth-weight pregnancy, and we developed an approach to identify the components of metal mixtures that colocalized with biological response markers. Our novel workflow, which includes custom-developed software tools and algorithms for spatial outlier identification and background subtraction in multidimensional elemental image stacks, enables rapid image processing and seamless integration of data from elemental imaging and immunohistochemistry. Using quantitative spatial statistics, we identified distinct patterns of metal accumulation at sites of inflammation. Broadly, our multiplexed approach can be used to explore the mechanisms mediating complex metal exposures and biologic responses within placentae and other tissue types. Our LA-ICP-MS image processing workflow can be accessed through our interactive R Shiny application 'shinyImaging', which is available at or through our laboratory's website, .

Left ventricle segmentation in fetal echocardiography using a multi-texture active appearance model based on the steered Hermite transform.

OBJECTIVE: Fetal echocardiographic analysis is essential for detecting cardiac defects at early gestational ages. Fetal cardiac function can be assessed by performing some measurements regarding the dimension and shape of the heart cavities. In this work we propose an automatic segmentation method applied to the analysis of the left ventricle in fetal echocardiography. METHODS: For segmentation of the left ventricle, we designed a novel multi-texture active appearance model (AAM) based on the Hermite transform (HT). Local orientation analysis is addressed by steering the coefficients obtained with the HT. The method basically consists of an AAM-based scheme which uses the steered HT to efficiently code texture patterns of the input image. A wider and detailed description of the image features can be obtained with this method. Compared with classic AAM methods, the segmentation performance is substantially improved with the proposed scheme. Since AAM-based approaches process local information, an automatic method is also proposed to initialize the multi-texture AAM. For this purpose, a database of pre-segmented images was built. Then, techniques such as thresholding, mathematical morphology and correlation are combined to identify the position and orientation of the left ventricle. Typical issues found in fetal cardiac ultrasound images such as different orientations and shape variations of the heart cavities can be easily handled with the designed method. RESULTS: Several images of fetal echocardiography were used to evaluate the proposed segmentation method. The algorithm performance was validated using different metrics. We used a database of 143 real images of fetal hearts acquired for different phases of the cardiac cycle. We obtained an average Dice coefficient of 0.8631 and a point-to-curve distance of 2.027 pixels. The proposed algorithm was also validated by comparing it with other segmentation methods. CONCLUSIONS: We have designed an automatic algorithm for left ventricle segmentation in fetal echocardiography. The reported results demonstrate that the proposed approach can achieve an efficient segmentation of the left ventricular cavity. Typical problems found in images of fetal echocardiography are satisfactorily handled with the proposed multi-texture AAM scheme.

Effect of late-onset fetal growth restriction on organization of behavioral state in infants.

BACKGROUND: The aim of this study was to analyze whether late-onset fetal growth restriction (FGR) alters regulatory capability in infants, and whether this can be detected using both the neonatal behavior assessment scale (NBAS) and brainstem auditory-evoked potentials (BAEP). METHODS: The diagnosis of FGR was made on Doppler examination in the third trimester of pregnancy. NBAS and BAEP measurement were performed at 1 month of corrected postnatal age. RESULTS: The group with late-onset FGR was integrated with 17 infants and the control group consisted of 14 subjects. The NBAS range of state score, which reflects organization of behavioral state, was low in infants with late-onset FGR. No differences were found in BAEP between groups. No association between NBAS and BAEP was detected. CONCLUSION: Late-onset FGR has a deleterious effect on NBAS range of state, but possibly does not alter BAEP response. It is proposed that regulatory capabilities in the neonatal period play a primary role in subtle cognitive difficulties in infants with late-onset FGR in the long term.

Studying the complex expression dependences between sets of coexpressed genes.

Organisms simplify the orchestration of gene expression by coregulating genes whose products function together in the cell. The use of clustering methods to obtain sets of coexpressed genes from expression arrays is very common; nevertheless there are no appropriate tools to study the expression networks among these sets of coexpressed genes. The aim of the developed tools is to allow studying the complex expression dependences that exist between sets of coexpressed genes. For this purpose, we start detecting the nonlinear expression relationships between pairs of genes, plus the coexpressed genes. Next, we form networks among sets of coexpressed genes that maintain nonlinear expression dependences between all of them. The expression relationship between the sets of coexpressed genes is defined by the expression relationship between the skeletons of these sets, where this skeleton represents the coexpressed genes with a well-defined nonlinear expression relationship with the skeleton of the other sets. As a result, we can study the nonlinear expression relationships between a target gene and other sets of coexpressed genes, or start the study from the skeleton of the sets, to study the complex relationships of activation and deactivation between the sets of coexpressed genes that carry out the different cellular processes present in the expression experiments.

Analysis of gene expression for studying tumor progression: the case of glucocorticoid administration.

BACKGROUND: Glucocorticoids are commonly used as adjuvant treatment for side-effects and have anti-proliferative activity in several tumors but, on the other hand, their proliferative effect has been reported in several studies, some of them involving the spread of cancer. We shall attempt to reconcile these incongruities from the genomic and tissue-physiology perspectives with our findings. METHODS: An accurate phenotype analysis of microarray data can help to solve multiple paradoxes derived from tumor-progression models. We have developed a new strategy to facilitate the study of interdependences among the phenotypes defined by the sample clusters obtained by common clustering methods (HC, SOTA, SOM, PAM). These interdependences are obtained by the detection of non-linear expression-relationships where each fluctuation in the relationship implies a phenotype change and each relationship typology implies a specific phenotype interdependence. As a result, multiple phenotypic changes are identified together with the genes involved in the phenotype transitions. In this way, we study the phenotypic changes from microarray data that describe common phenotypes in cancer from different tissues, and we cross our results with biomedical databases to relate the glucocorticoid activity to the phenotypic changes. RESULTS: 11,244 significant non-linear expression relationships, classified into 11 different typologies, have been detected from the data matrix analyzed. From them, 415 non-linear expression relationships were related to glucocorticoid activity. Studying them, we have found the possible reason for opposite effects of some stressor agents like dexamethasone on tumor progression and it has been confirmed by literature. This hidden reason has resulted in being linked with the type of tumor progression of the tissues. In the first type of tumor progression found, new cells can be stressed during proliferation and stressor agents increase tumor proliferation. In the second type, cell stress and tumor proliferation are antagonists so, therefore, stressor agents stop tumor proliferation in order to stress the cells. The non-linear expression relationships among DUSP6, FERMT2, FKBP5, EGFR, NEDD4L and CITED2 genes are used to synthesize these findings.