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Lymphoma infiltration to the pituitary is rare. It represents less than 0.5% of all reported pituitary metastases (PMs). Here we present a case series of 3 patients with PMs from a systemic lymphoma. Also, we performed a literature review of the cases reported. We identified additional 31 cases in which non-Hodgkin lymphoma (NHL) was the most common (n = 28, 90%), with large B-cell NHL the most frequent histological subtype (n = 14, 45%). Central hypothyroidism (n = 21, 67%) was the most frequent pituitary deficiency followed by adrenal insufficiency (n = 19, 61%) and diabetes insipidus (DI; n = 18, 58%). Full endocrine recovery was found in only 12% (n = 4) of patients after treatment, and magnetic resonance imaging showed tumor regression in 22% of them. In our series, 2 patients were diagnosed with diffuse large B-cell lymphoma, and 1 had mixed cellularity of classic Hodgkin lymphoma. The mean age was 54 ± 6.92 years. Hypopituitarism and DI were present in all of them, with 100% of mortality because of advanced systemic disease.

Genome-wide association studies (GWAS) have allowed the identification of different loci associated with primary root (PR) growth, and Arabidopsis is an excellent model for these studies. The PR length is controlled by cell proliferation, elongation, and differentiation; however, the specific contribution of proliferation and differentiation in the control of PR growth is still poorly studied. To this end, we analyzed 124 accessions and used a GWAS approach to identify potential causal genomic regions related to four traits: PR length, growth rate, cell proliferation and cell differentiation. Twenty-three genes and five statistically significant SNPs were identified. The SNP with the highest score mapped to the fifth exon of

OBJECTIVE: Sarcopenia has been related to negative outcomes in different clinical scenarios from critical illness to chronic conditions. The aim of this study was to verify whether there was an association between low skeletal muscle index and in-hospital mortality, intensive care unit admission, and invasive mechanical ventilation need in hospitalized patients with COVID-19. DESIGN: This was a retrospective cohort study of a referral center for COVID-19. We included all consecutive patients admitted to the hospital between February 26 and May 15, 2020, with a confirmed diagnosis of COVID-19. Skeletal muscle index was assessed from a transverse computed tomography image at the level of twelfth thoracic vertebra with National Institutes of Health ImageJ software, and statistical analysis was performed to find an association between skeletal muscle index and in-hospital mortality, need of invasive mechanical ventilation, and intensive care unit admission. RESULTS: We included 519 patients, the median age was 51 (42-61) yrs, and 115 patients (22%) had low skeletal muscle index. On multivariable analysis, skeletal muscle index was not associated with mortality, intensive care unit admission, or invasive mechanical ventilation need nor in a subanalysis of patients 65 yrs or older. CONCLUSIONS: Skeletal muscle index determined by computed tomography at the level of twelfth thoracic vertebra was not associated with negative outcomes in hospitalized patients with COVID-19.

Expression microarrays are powerful technology that allows large-scale analysis of RNA profiles in a tissue; these platforms include underexploited detection scores outputs. We developed an algorithm using the detection score, to generate a detection profile of shared elements in retinoblastoma as well as to determine its transcriptomic size and structure. We analyzed eight briefly cultured primary retinoblastomas with the Human transcriptome array 2.0 (HTA2.0). Transcripts and genes detection scores were determined using the Detection Above Background algorithm (DABG). We used unsupervised and supervised computational tools to analyze detected and undetected elements; WebGestalt was used to explore functions encoded by genes in relevant clusters and performed experimental validation. We found a core cluster with 7,513 genes detected and shared by all samples, 4,321 genes in a cluster that was commonly absent, and 7,681 genes variably detected across the samples accounting for tumor heterogeneity. Relevant pathways identified in the core cluster relate to cell cycle, RNA transport, and DNA replication. We performed a kinome analysis of the core cluster and found 4 potential therapeutic kinase targets. Through analysis of the variably detected genes, we discovered 123 differentially expressed transcripts between bilateral and unilateral cases. This novel analytical approach allowed determining the retinoblastoma transcriptomic size, a shared active transcriptomic core among the samples, potential therapeutic target kinases shared by all samples, transcripts related to inter tumor heterogeneity, and to determine transcriptomic profiles without the need of control tissues. This approach is useful to analyze other cancer or tissue types.

High-throughput genomic technologies have revolutionized the study of cancer. Current research in oncology is now limited more for the capacity of analyzing and interpreting data, rather than the availability of data itself. Integrative approaches to obtain functional information from data are at the core of the disciplines gathered under the systems biology banner. In this context, network models have been used to study cancer, from the identification of key molecules involved in the disease to the discovery of functional alterations associated with specific manifestations of the disease.In this chapter, we describe the state of the art of network reconstruction from genomic data, with an emphasis in gene expression experiments. We explore the strengths and limitations of correlation, Bayesian, and information theoretic approaches to network reconstruction. We present tools that leverage the flexibility of network science to gain a deeper understanding of cancer biology.

Malignant lymphoma of the pituitary gland is extremely rare. The most common form of CNS involvement in lymphoma is leptomeningeal metastases, but it can also affect the spine or the cerebral parenchyma. Pituitary involvement usually presents as diabetes insipidus but can also present as hypopituitarism if there is adenohypophyseal destruction and may also present as bitemporal hemianopsia. (1)We present a case series of four patients with metastatic lymphomas to the pituitary gland that presented with diabetes insipidus to a tertiary center in Mexico City. Our investigation identified 4 patients with a previous diagnosis of lymphoma (3 B cell non-Hodgkin and 1 Hodgkin lymphoma) that were admitted into the endocrinology clinic with evidence of pituitary involvement and metastases to the pituitary gland. The average age of the cases was 54.5 years. 1 of the 4 patients had a diagnosis of HIV infections prior to the diagnosis of the lymphoma. Diabetes insipidus was present in 100% of the patients, while headache (75%) and visual field defects (50%) were the most common findings at presentation. Two of the patients also had involvement of the adenohypophysis and had hypocortisolism and central hypothyroidism that required hormone replacement therapy. All cases were documented by a lesion in the contrast enhancing lesion on MRI. Lymphoma involving the pituitary gland is particularly rare. In our series, involvement of the posterior pituitary was the most common form of presentation in contrast to the reported in other series, but the clinical presentation can be heterogenous, hence, it is important to perform an extensive endocrinological examination when pituitary involvement is suspected.1. Mathiasen RA, Jarrahy R, Cha ST, et al. Pituitary lymphoma: a case report and literature review. Pituitary. 2000;2(4): 283-287. doi: 10.1023/a: 1009969417380 Presentation: No date and time listed

Retinoblastoma (Rb) is a rare intraocular tumour in early childhood, with an approximate incidence of 1 in 18 000 live births. Experimental studies for Rb are complex due to the challenges associated with obtaining a normal retina to contrast with diseased tissue. In this work, we reanalyse a dataset that contains normal retina samples. We identified the individual genes whose expression is different in Rb in contrast with normal tissue, determined the pathways whose global expression pattern is more distant from the global expression observed in normal tissue, and finally, we identified which transcription factors regulate the highest number of differentially expressed genes (DEGs) and proposed as transcriptional master regulators (TMRs). The enrichment of DEGs in the phototransduction and retrograde endocannabinoid signalling pathways could be associated with abnormal behaviour of the processes leading to cellular differentiation and cellular proliferation. On the other hand, the TMRs nuclear receptor subfamily 5 group A member 2 and hepatocyte nuclear factor 4 gamma are involved in hepatocyte differentiation. Therefore, the enrichment of aberrant expression in these transcription factors could suggest an abnormal retina development that could be involved in Rb origin and progression.

Genome-wide association studies (GWAS) have allowed the identification of different loci associated with primary root (PR) growth, and Arabidopsis is an excellent model for these studies. The PR length is controlled by cell proliferation, elongation, and differentiation; however, the specific contribution of proliferation and differentiation in the control of PR growth is still poorly studied. To this end, we analyzed 124 accessions and used a GWAS approach to identify potential causal genomic regions related to four traits: PR length, growth rate, cell proliferation and cell differentiation. Twenty-three genes and five statistically significant SNPs were identified. The SNP with the highest score mapped to the fifth exon of NAC048 and this change makes a missense variant in only 33.3% of the accessions with a large PR, compared with the accessions with a short PR length. Moreover, we detected five more SNPs in this gene and in NAC3 that allow us to discover closely related accessions according to the phylogenetic tree analysis. We also found that the association between genetic variants among the 18 genes with the highest scores in our GWAS and the phenotypic classes into which we divided our accessions are not straightforward and likely follow historical patterns.

Background The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is a current public health concern. Rapid diagnosis is crucial, and reverse transcription polymerase chain reaction (RT-PCR) is presently the reference standard for SARS-CoV-2 detection. Objective Automated RT-PCR analysis (ARPA) is a software designed to analyze RT-PCR data for SARSCoV-2 detection. ARPA loads the RT-PCR data, classifies each sample by assessing its amplification curve behavior, evaluates the experiment's quality, and generates reports. Methods ARPA was implemented in the R language and deployed as a Shiny application. We evaluated the performance of ARPA in 140 samples. The samples were manually classified and automatically analyzed using ARPA. Results ARPA had a true-positive rate = 1, true-negative rate = 0.98, positive-predictive value = 0.95, and negative-predictive value = 1, with 36 samples correctly classified as positive, 100 samples correctly classified as negative, and two samples classified as positive even when labeled as negative by manual inspection. Two samples were labeled as invalid by ARPA and were not considered in the performance metrics calculation. Conclusions ARPA is a sensitive and specific software that facilitates the analysis of RT-PCR data, and its implementation can reduce the time required in the diagnostic pipeline.

Studies have suggested a potential role of somatic mitochondrial mutations in cancer development. To analyze the landscape of somatic mitochondrial mutation in breast cancer and to determine whether mitochondrial DNA (mtDNA) mutational burden is correlated with overall survival (OS), we sequenced whole mtDNA from 92 matched-paired primary breast tumors and peripheral blood. A total of 324 germline variants and 173 somatic mutations were found in the tumors. The most common germline allele was 663G (12S), showing lower heteroplasmy levels in peripheral blood lymphocytes than in their matched tumors, even reaching homoplasmic status in several cases. The heteroplasmy load was higher in tumors than in their paired normal tissues. Somatic mtDNA mutations were found in 73.9% of breast tumors; 59% of these mutations were located in the coding region (66.7% non-synonymous and 33.3% synonymous). Although the CO1 gene presented the highest number of mutations, tRNA genes (T,C, and W), rRNA 12S, and CO1 and ATP6 exhibited the highest mutation rates. No specific mtDNA mutational profile was associated with molecular subtypes of breast cancer, and we found no correlation between mtDNA mutational burden and OS. Future investigations will provide insight into the molecular mechanisms through which mtDNA mutations and heteroplasmy shifting contribute to breast cancer development.

Background Hypertension affects 33% of Americans while type 2 diabetes and Alzheimer's disease (AD) affect 10% of Americans, respectively. Ryanodine receptor 3 gene (RYR3) codes for the RYR which functions to release stored endoplasmic reticulum calcium ions (Ca2+) to increase intracellular Ca2+ concentration. Increasing studies demonstrate that altered levels of intracellular Ca2+ affect cardiac contraction, insulin secretion, and neurodegeneration. In this study, we investigated associations of the RYR3 genetic variants with hypertension, AD, and diabetes. Methods Family data sets were used to explore association of RYR3 polymorphisms with risk and age at onset (AAO) of hypertension, diabetes, and AD. Results Family-based association tests using generalized estimating equations (FBAT-GEE) showed several unique or shared disease-1 associated variants in the RYR3 gene. Three single nuclear polymorphisms (SNPs; rs2033610, rs2596164, and rs2278317) are significantly associated with risk for hypertension, diabetes, and AD. Two SNPs (rs4780174 and rs7498093) are significantly associated with AAO of the 3 diseases. Conclusions RYR3 variants are associated with hypertension, diabetes, and AD. Replication of these results of this gene in these 3 complex traits may help to better understand the genetic basis of calcium-signaling gene, RYR3 in association with risk and AAO of these diseases.

Expression microarrays are powerful technology that allows large-scale analysis of RNA profiles in a tissue; these platforms include underexploited detection scores outputs. We developed an algorithm using the detection score, to generate a detection profile of shared elements in retinoblastoma as well as to determine its transcriptomic size and structure.We analyzed eight briefly cultured primary retinoblastomas with the Human transcriptome array 2.0 (HTA2.0). Transcripts and genes detection scores were determined using the Detection Above Background algorithm (DABG). We used unsupervised and supervised computational tools to analyze detected and undetected elements; WebGestalt was used to explore functions encoded by genes in relevant clusters and performed experimental validation.We found a core cluster with 7,513 genes detected and shared by all samples, 4,321 genes in a cluster that was commonly absent, and 7,681 genes variably detected across the samples accounting for tumor heterogeneity. Relevant pathways identified in the core cluster relate to cell cycle, RNA transport, and DNA replication. We performed a kinome analysis of the core cluster and found 4 potential therapeutic kinase targets. Through analysis of the variably detected genes, we discovered 123 differentially expressed transcripts between bilateral and unilateral cases.This novel analytical approach allowed determining the retinoblastoma transcriptomic size, a shared active transcriptomic core among the samples, potential therapeutic target kinases shared by all samples, transcripts related to inter tumor heterogeneity, and to determine transcriptomic profiles without the need of control tissues. This approach is useful to analyze other cancer or tissue types.

Transcription factors are responsible for both tissue-specific gene expression and the regulation of gene activity in response to specific stimuli. Analysis of whole genome gene expression data and studies on the specificities of transcription factor binding to genomic DNA sequences are common ways of inferring transcriptional regulatory networks. The interplay between different levels of regulation occurs when a series of transcription factors triggers a gene cascade that controls the whole regulatory program for cells under associated phenotypes. In this chapter, we discuss the role that transcription factors and other related molecules have on the intricate series of processes leading to genome-wide transcriptional regulatory programs. Experimental, computational, and modeling approaches to this problem are presented, and some general methods and algorithms are described in greater detail. Finally, we present an overview section on how to incorporate epigenomic and other regulatory processes in the context of networks of transcription factors.

Analysis of gene regulatory networks allows the identification of master transcriptional factors that control specific groups of genes. In this work, we inferred a gene regulatory network from a large dataset of breast cancer samples to identify the master transcriptional factors that control the genes within signal transduction pathways. The focus in a particular subset of relevant genes constitutes an extension of the original Master Regulator Inference Algorithm (MARINa) analysis. This modified version of MARINa utilizes a restricted molecular signature containing genes from the 25 human pathways in KEGG's signal transduction category. Our breast cancer RNAseq expression dataset consists of 881 samples comprising tumors and normal mammary gland tissue. The top 10 master transcriptional factors found to regulate signal transduction pathways in breast cancer we identified are: TSHZ2, HOXA2, MEIS2, HOXA3, HAND2, HOXA5, TBX18, PEG3, GLI2, and CLOCK. The functional enrichment of the regulons of these master transcriptional factors showed an important proportion of processes related to morphogenesis. Our results suggest that, as part of the aberrant regulation of signaling pathways in breast cancer, pathways similar to the regulation of cell differentiation, cardiovascular system development, and vasculature development may be dysregulated and co-opted in favor of tumor development through the action of these transcription factors.

Facilitation by tussocks is common in high-altitude tropical environments. It is thought that facilitation results from stress amelioration, but it is unclear which of the many stressors acting in these environments is ameliorated. We aimed at determining the relative importance of different stressors as drivers of facilitation by the tussock Festuca tolucensis in Mexico. We employed eight experimental treatments to manipulate five stressors in the field: minimum temperatures by using electric radiators that kept plants warm; maximum temperatures by means of reflective sand that precluded temperature build-up during the day; UV radiation by using screens opaque to UV; poor soil properties by comparing soils from beneath tussocks and from bare ground; and low water availability by adding vermiculite to the soil. The performance (survival and growth) of Mexerion sarmentosum (a plant usually associated with Festuca) in these treatments was compared to that recorded under tussocks and in bare ground. Amelioration of extreme temperatures had the largest positive effects on Mexerion survival. UV radiation and increased soil humidity did not affect survival, although humidity increased growth rates. Nevertheless, tussocks reduced the growth of Mexerion, which is consistent with observations of competition between plants and soil microorganisms favoured by tussocks. Our results highlight the importance of the extreme daily fluctuations in temperature that characterise tropical mountains as fundamental drivers of their dynamics.

In this work, we evaluated several tools used for the alignment of short sequences and found that most aligners execute reasonably well for identical sequences, whereas a variety of alignment errors emerge for dissimilar ones. Since alignments are essential in computational biology, we developed ExtendAlign, a post-analysis tool that corrects these errors and improves the alignment of dissimilar short sequences. We used simulated and biological data to show that ExtendAlign outperforms the other aligners in most metrics tested. ExtendAlign is useful for pinpointing the identity percentage for alignments of short sequences in the range of ∼35–50% similarity.

Meetings frequently involve discussion of documents and can be significantly affected if a document is absent. An agent system capable of spontaneously retrieving a document at the point it is needed would have to judge whether a meeting is talking about a particular document and whether that document is already present. We report the exploratory application of agent techniques for making these two judgements. To obtain examples from which an agent system can learn, we first conducted a study of participants making these judgements with video recordings of meetings. We then show that interactions between hands and paper documents in meetings can be used to recognise when a document being talked about is not to hand. The work demonstrates the potential for multimodal agent systems using these techniques to learn to perform specific, discourse-level tasks during meetings.

Signal transduction pathways allow cells to respond to environmental cues and can induce intracellular changes. In some contexts, like embryonic development, signal transduction plays crucial roles in cell fate determination and differentiation, while in developed organisms some of this processes contribute in the maintenance of the structural integrity of tissues.Tumor cells are recognized as having deregulated signaling which leads to a series of abnormal behaviors known as the hallmarks of cancer. Although gene regulation is often viewed as the last step in signal transduction, transcriptional regulation of the components of a pathway may impact in the long term deregulation observed in tumors. The study of gene regulatory networks centered around genes of the signal transduction pathways allows the identification of transcriptional regulators with the greatest influence over the signal transduction gene signature, also denominated Master Regulators.In this work we identify, the master regulators that regulate the expression of genes of 25 relevant pathways grouped in KEGG within the category of signal transduction in a breast cancer dataset. For this purpose we implemented a modified MARINa algorithm that identifies, from a network of regulons, those that possess more differentially expressed genes related to the process to be studied. We identified CLOCK, TSHZ2, HOXA2, MEIS2, HOXA3, HAND2, HOXA5, TBX18, PEG3 and GLI2 as the top 10 master regulators of signaling pathways in breast cancer. Nine of them are recognized for taking part in embryonic development associated processes.Individual enrichment GO biological function for each TMR regulons showed to be significantly enriched in embryonic development related processes. Hedgehog signaling pathway was shown as enriched and also highly deregulated. The genes of the HOXA family are shared among most of the TMRs. Overall, this suggests the importance of the aberrant reprogramming of mechanisms present during embryonic development, being coopted in favor of tumor development.

Graphical abstractDisplay Omitted Gene regulatory networks account for the delicate mechanisms that control gene expression. Under certain circumstances, gene regulatory programs may give rise to amplification cascades. Such transcriptional cascades are events in which activation of key-responsive transcription factors called master regulators trigger a series of gene expression events. The action of transcriptional master regulators is then important for the establishment of certain programs like cell development and differentiation. However, such cascades have also been related with the onset and maintenance of cancer phenotypes. Here we present a systematic implementation of a series of algorithms aimed at the inference of a gene regulatory network and analysis of transcriptional master regulators in the context of primary breast cancer cells. Such studies were performed in a highly curated database of 880 microarray gene expression experiments on biopsy-captured tissue corresponding to primary breast cancer and healthy controls. Biological function and biochemical pathway enrichment analyses were also performed to study the role that the processes controlled - at the transcriptional level - by such master regulators may have in relation to primary breast cancer. We found that transcription factors such as AGTR2, ZNF132, TFDP3 and others are master regulators in this gene regulatory network. Sets of genes controlled by these regulators are involved in processes that are well-known hallmarks of cancer. This kind of analyses may help to understand the most upstream events in the development of phenotypes, in particular, those regarding cancer biology.

Gene expression in eukaryotic cells is an extremely complex and interesting phenomenon whose dynamics are controlled by a large number of subtle physicochemical processes commonly described by means of gene regulatory networks. Such networks consist in a series of coupled chemical reactions, conformational changes, and other biomolecular processes involving the interaction of the DNA molecule itself with a number of proteins usually called transcription factors as well as enzymes and other components. The kinetics behind the functioning of such gene regulatory networks are largely unknown, though its description in terms of non-equilibrium thermodynamics has been discussed recently. In this work we will derive general kinetic equations for a gene regulatory network from a non-equilibrium thermodynamical description and discuss its use in understanding the free energy constrains imposed in the network structure. We also will discuss explicit expressions for the kinetics of a simple model of gene regulation and show that the kinetic role of mRNA decay during the RNA synthesis stage (or transcription) is somehow limited due to the comparatively low values of decay rates. At the level discussed here, this implies a decoupling of the kinetics of mRNA synthesis and degradation a fact that may become quite useful when modeling gene regulatory networks from experimental data on whole genome gene expression.

Introduction: Retinoblastoma (Rb) is an intraocular malignant tumour of early childhood, considered the most robust clinical model of genetic predisposition to develop cancer. Previous transcriptomic studies in Rb are based on differential analysis of expression data, ignoring the detection scores that can be obtained from high throughput technologies. The detection score allows to obtain profiles of present/absent mRNAs across samples without the need of a control sample. In this study, 8 primary cultured tumors and Rb cell line Y79 were analyzed using the detection scores, with the aim to discover what is similar and what is different among them in terms of mRNA detection. Methodology: Tumors were obtained from enucleated eyes of Rb patients treated at Hospital de Pediatría, CMN SXXI, Instituto Mexicano Del Seguro Social (IMSS) and Hospital Infantil de México Federico Gómez, Secretaría de Salud in Mexico City. Tumor tissues were cultured for a week and total RNA extracted. We used Affymetrix Human Transcriptome Array 2.0 able to detect many isoforms. Pre-processing included normalization with RMA (Average Robust Multiarray) and DABG (Detection Above Background) to calculate the detection score for each exon as present or absent. We carried out subsequent analysis to determine the detection score of each transcript and each gene in two corresponding datasets. For each of these detection datasets, hierarchical clustering (HC) was performed producing two color detection maps for present and absent elements. Results: We obtained detection scores for 21,986 genes and 59,629 annotated transcripts (isoforms). In the detection map of genes obtained with HC, we discovered a central core of 3,391 mRNAs cluster shared by all the samples, while in the detection map of transcripts we discovered a 12,799 mRNAs cluster shared by all the samples. Absent mRNAs cluster were also shared across the samples with 9,005 in the genes and 18,651 in the transcripts detection maps. Notably, variability accounted for a cluster of 9,590 and 28,179 across the genes and transcripts detection maps, respectively. Furthermore, we determined the Rb transcriptomic size by counting the mRNAs in each sample and calculating the average across the datasets with 8,238 and 28,396 mRNA detected, 13,747 and 31,232 not detected corresponding to gene or transcript detection maps. When Y79 was introduced in the datasets, these numbers change considerably, and the corresponding detection maps show mRNA clusters specific for the cell line, but not present in the primary tissues. Conclusion: Rb samples shared a central mRNA cluster of detected elements and a cluster of not detected elements, despite tumor heterogeneity. This systems level analysis brings a coherent and easy way for biological interpretation of high throughput data. Note: This abstract was not presented at the meeting. Citation Format: Diana E. Alvarez Suarez, Hugo Tovar, Enrique Hernández, Manuela Orjuela, Lourdes Cabrera, Claudia Hernández, Daphne García, Adriana Hernández, Verónica Ponce. Transcriptomic analysis of primary retinoblastoma with affymetrix HTA2 chip using a detection score reveals a central core of mRNA shared by all the samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 5143.

Gene regulatory networks present a wide variety of dynamical responses to intrinsic and extrinsic perturbations. An outstanding case of such coordinated responses is that of transcriptional amplification cascades, in which activation of a few key-responsive transcription factors (termed master regulators) leads to a large series of transcriptional activation events. Recent studies have pointed to the protein called myocyte enhancing factor 2C (MEF2C) as being one of such master regulators involved in the pathogenesis of primary breast cancer. A systems biology analysis of the transcriptional regulation activity of MEF2C and its target genes, has revealed that this molecule induces collective responses leading to system-level gene expression deregulation and carcinogenesis. We found extensive evidence for this. Being this the case, one may wonder what set of physicochemical, structural and thermodynamic constrains need to be satisfied if a protein is to become a transcription factor, and moreover a master regulator? Some hints will be discussed.