Deciphering gene contributions and etiologies of somatic mutational signatures of cancer.

Somatic mutational signatures (MSs) identified by genome sequencing play important roles in exploring the cause and development of cancer. Thus far, many such signatures have been identified, and some of them do imply causes of cancer. However, a major bottleneck is that we do not know the potential meanings (i.e. carcinogenesis or biological functions) and contributing genes for most of them. Here, we presented a computational framework, Gene Somatic Genome Pattern (GSGP), which can decipher the molecular mechanisms of the MSs. More importantly, it is the first time that the GSGP is able to process MSs from ribonucleic acid (RNA) sequencing, which greatly extended the applications of both MS analysis and RNA sequencing (RNAseq). As a result, GSGP analyses match consistently with previous reports and identify the etiologies for a number of novel signatures. Notably, we applied GSGP to RNAseq data and revealed an RNA-derived MS involved in deficient deoxyribonucleic acid mismatch repair and microsatellite instability in colorectal cancer. Researchers can perform customized GSGP analysis using the web tools or scripts we provide.

DVA: predicting the functional impact of single nucleotide missense variants.

BACKGROUND: In the past decade, single nucleotide variants (SNVs) have been identified as having a significant relationship with the development and treatment of diseases. Among them, prioritizing missense variants for further functional impact investigation is an essential challenge in the study of common disease and cancer. Although several computational methods have been developed to predict the functional impacts of variants, the predictive ability of these methods is still insufficient in the Mendelian and cancer missense variants. RESULTS: We present a novel prediction method called the disease-related variant annotation (DVA) method that predicts the effect of missense variants based on a comprehensive feature set of variants, notably, the allele frequency and protein-protein interaction network feature based on graph embedding. Benchmarked against datasets of single nucleotide missense variants, the DVA method outperforms the state-of-the-art methods by up to 0.473 in the area under receiver operating characteristic curve. The results demonstrate that the proposed method can accurately predict the functional impact of single nucleotide missense variants and substantially outperforms existing methods. CONCLUSIONS: DVA is an effective framework for identifying the functional impact of disease missense variants based on a comprehensive feature set. Based on different datasets, DVA shows its generalization ability and robustness, and it also provides innovative ideas for the study of the functional mechanism and impact of SNVs.

SPIDE: A single cell potency inference method based on the local cell-specific network entropy.

For a given single cell RNA-seq data, it is critical to pinpoint key cellular stages and quantify cells' differentiation potency along a differentiation pathway in a time course manner. Currently, several methods based on the entropy of gene functions or PPI network have been proposed to solve the problem. Nevertheless, these methods still suffer from the inaccurate interactions and noises originating from scRNA-seq profile. In this study, we proposed a cell potency inference method based on cell-specific network entropy, called SPIDE. SPIDE introduces the local weighted cell-specific network for each cell to maintain cell heterogeneity and calculates the entropy by incorporating gene expression with network structure. In this study, we compared three cell entropy estimation models on eight scRNA-Seq datasets. The results show that SPIDE obtains consistent conclusions with real cell differentiation potency on most datasets. Moreover, SPIDE accurately recovers the continuous changes of potency during cell differentiation and significantly correlates with the stemness of tumor cells in Colorectal cancer. To conclude, our study provides a universal and accurate framework for cell entropy estimation, which deepens our understanding of cell differentiation, the development of diseases and other related biological research.

Application of Single-Cell Assay for Transposase-Accessible Chromatin with High Throughput Sequencing in Plant Science: Advances, Technical Challenges, and Prospects.

The Single-cell Assay for Transposase-Accessible Chromatin with high throughput sequencing (scATAC-seq) has gained increasing popularity in recent years, allowing for chromatin accessibility to be deciphered and gene regulatory networks (GRNs) to be inferred at single-cell resolution. This cutting-edge technology now enables the genome-wide profiling of chromatin accessibility at the cellular level and the capturing of cell-type-specific cis-regulatory elements (CREs) that are masked by cellular heterogeneity in bulk assays. Additionally, it can also facilitate the identification of rare and new cell types based on differences in chromatin accessibility and the charting of cellular developmental trajectories within lineage-related cell clusters. Due to technical challenges and limitations, the data generated from scATAC-seq exhibit unique features, often characterized by high sparsity and noise, even within the same cell type. To address these challenges, various bioinformatic tools have been developed. Furthermore, the application of scATAC-seq in plant science is still in its infancy, with most research focusing on root tissues and model plant species. In this review, we provide an overview of recent progress in scATAC-seq and its application across various fields. We first conduct scATAC-seq in plant science. Next, we highlight the current challenges of scATAC-seq in plant science and major strategies for cell type annotation. Finally, we outline several future directions to exploit scATAC-seq technologies to address critical challenges in plant science, ranging from plant ENCODE(The Encyclopedia of DNA Elements) project construction to GRN inference, to deepen our understanding of the roles of CREs in plant biology.

The Transcriptional Landscape of Polyploid Wheats and Their Diploid Ancestors during Embryogenesis and Grain Development.

Modern wheat production comes from two polyploid species, Triticum aestivum and Triticum turgidum (var durum), which putatively arose from diploid ancestors Triticum urartu, Aegilops speltoides, and Aegilops tauschii How gene expression during embryogenesis and grain development in wheats has been shaped by the differing contributions of diploid genomes through hybridization, polyploidization, and breeding selection is not well understood. This study describes the global landscape of gene activities during wheat embryogenesis and grain development. Using comprehensive transcriptomic analyses of two wheat cultivars and three diploid grasses, we investigated gene expression at seven stages of embryo development, two endosperm stages, and one pericarp stage. We identified transcriptional signatures and developmental similarities and differences among the five species, revealing the evolutionary divergence of gene expression programs and the contributions of A, B, and D subgenomes to grain development in polyploid wheats. The characterization of embryonic transcriptional programming in hexaploid wheat, tetraploid wheat, and diploid grass species provides insight into the landscape of gene expression in modern wheat and its ancestral species. This study presents a framework for understanding the evolution of domesticated wheat and the selective pressures placed on grain production, with important implications for future performance and yield improvements.plantcell;31/12/2888/FX1F1fx1.

Malnutrition as a risk factor of adverse postoperative outcomes in patients undergoing hepatic resection: analysis of US hospitals.

Patients with liver cancer or space-occupying cysts suffer from malnutrition due to compression of gastric and digestive structures, liver and cancer-mediated dysmetabolism, and impaired nutrient absorption. As proportion of these patients requires removal of lesions through hepatic resection, it is important to evaluate the effects of malnutrition on post-hepatectomy outcomes. In our study approach, 2011-2017 National Inpatient Sample was used to isolate in-hospital hepatectomy cases, which were stratified using malnutrition (composite of malnutrition, sarcopenia and weight loss/cachexia). The malnutrition-absent controls were matched to cases using nearest neighbour propensity score matching method and compared with the following endpoints: mortality, length of stay, hospitalisation costs and postoperative complications. There were 2531 patients in total who underwent hepatectomy with matched number of controls from the database; following the match, malnutrition cohort (compared with controls) was more likely to experience in-hospital death (6·60 % v. 5·25 % P < 0·049, OR 1·27, 95 % CI 1·01, 1·61) and was more likely to have higher length of stay (18·10 d v. 9·32 d, P < 0·001) and hospitalisation costs ($278 780 v. $150 812, P < 0·001). In terms of postoperative complications, malnutrition cohort was more likely to experience bleeding (6·52 % v. 3·87 %, P < 0·001, OR 1·73, 95 % CI 1·34, 2·24), infection (6·64 % v. 2·49 %, P < 0·001, OR 2·79, 95 % CI 2·07, 3·74), wound complications (4·5 % v. 1·38 %, P < 0·001, OR 3·36, 95 % CI 2·29, 4·93) and respiratory failure (9·40 % v. 4·11 %, P < 0·001, OR 2·42, 95 % CI 1·91, 3·07). In multivariate analysis, malnutrition was associated with higher mortality (P < 0·028, adjusted OR 1·3, 95 % CI 1·03, 1·65). Thus, we conclude that malnutrition is a risk factor of postoperative mortality in patients undergoing hepatectomy.

The impact of frailty on the postoperative outcomes of patients undergoing cholecystectomy: propensity score matched analysis of 2011-2017 US hospitals.

BACKGROUND: Frailty is an aggregate variable that encompasses debilitating geriatric conditions, which potentially affects postoperative outcomes. In this study, we evaluate the relationship between clinical frailty and post-cholecystectomy outcomes using a national registry of hospitalized patients. METHODS: 2011-2017 National Inpatient Sample database was used to identify patients who underwent cholecystectomy. Patients were stratified using the Johns Hopkins ACG frailty definition into binary (frailty and no-frailty) and tripartite frailty (frailty, prefrailty, no-frailty) indicators. The controls were matched to study cohort using 1:1 propensity score-matching and postoperative outcomes were compared. RESULTS: Post-match, using the binary term, frail patients (n = 40,067) had higher rates of mortality (OR 2.07 95%CI 1.90-2.25), length of stay, costs, and complications. In multivariate, frailty was associated with higher mortality (aOR 2.06 95%CI 1.89-2.24). When using tripartite frailty term, prefrail (n = 35,595) and frail (n = 4472) patients had higher mortality (prefrailty: OR 2.04 95%CI 1.86-2.23; frailty: OR 2.49 95%CI 1.99-3.13), length of stay, costs, and complications. In multivariate, prefrailty and frailty were associated with higher mortality (prefrailty: aOR 2.02 95%CI 1.84-2.21; frailty: aOR 2.54 95%CI 2.02-3.19). CONCLUSION: This study shows the presence of frailty (and prefrailty) is an independent risk factor of adverse postoperative outcomes in patients undergoing cholecystectomy.

Examining the etiology of early-onset breast cancer in the Canadian Partnership for Tomorrow's Health (CanPath).

PURPOSE: Breast cancer incidence among younger women (under age 50) has increased over the past 25 years, yet little is known about the etiology among this age group. The objective of this study was to investigate relationships between modifiable and non-modifiable risk factors and early-onset breast cancer among three prospective Canadian cohorts. METHODS: A matched case-control study was conducted using data from Alberta's Tomorrow Project, BC Generations Project, and the Ontario Health Study. Participants diagnosed with breast cancer before age 50 were identified through provincial registries and matched to three control participants of similar age and follow-up. Conditional logistic regression was used to examine the association between factors and risk of early-onset breast cancer. RESULTS: In total, 609 cases and 1,827 controls were included. A body mass index ≥ 30 kg/m2 was associated with a lower risk of early-onset breast cancer (OR 0.65; 95% CI 0.47-0.90), while a waist circumference ≥ 88 cm was associated with an increased risk (OR 1.58; 95% CI 1.18-2.11). A reduced risk was found for women with ≥ 2 pregnancies (OR 0.76; 95% CI 0.59-0.99) and a first-degree family history of breast cancer was associated with an increased risk (OR 1.95; 95% CI 1.47-2.57). CONCLUSIONS: In this study, measures of adiposity, pregnancy history, and familial history of breast cancer are important risk factors for early-onset breast cancer. Evidence was insufficient to conclude if smoking, alcohol intake, fruit and vegetable consumption, and physical activity are meaningful risk factors. The results of this study could inform targeted primary and secondary prevention for early-onset breast cancer.

The relationship between mast cell activation syndrome, postural tachycardia syndrome, and Ehlers-Danlos syndrome.

Background: Postural tachycardia syndrome (POTS), hypermobile Ehlers-Danlos syndrome (EDS), and mast cell activation syndrome (MCAS) can occur in the same patient. In this study, we investigated the relationship among these three syndromes. Objective: To establish the relationship of MCAS in patients diagnosed with POTS and hypermobile EDS as well as characterize the demographics of the patients affected by these syndromes. Methods: A total of 195 medical records of patients by using a diagnostic codes data base search for disorders of autonomic dysfunction were identified. The demographics of the patients and diagnoses of POTS, EDS, or MCAS were recorded. Confidence intervals of the proportion of patients MCAS within a population of patients with POTS and EDS were compared with the proportion of patients with MCAS and without POTS and EDS. Odds ratios were also calculated within these groups. Results: The percentage of MCAS within the group of POTS and EDS was 31% in comparison with 2% within the non-POTS and EDS group. The 95% confidence interval calculated for the MCAS in the POTS and EDS group did not overlap with 2%, which showed a statistically significant result. The odds ratio between the two groups was found to be 32.46. Conclusion: There was a marked percentage of MCAS among the patients with diagnoses of POTS and EDS.

Mutational landscape differences between young-onset and older-onset breast cancer patients.

BACKGROUND: The incidence of breast cancer among young women (aged ≤40 years) has increased in North America and Europe. Fewer than 10% of cases among young women are attributable to inherited BRCA1 or BRCA2 mutations, suggesting an important role for somatic mutations. This study investigated genomic differences between young- and older-onset breast tumours. METHODS: In this study we characterized the mutational landscape of 89 young-onset breast tumours (≤40 years) and examined differences with 949 older-onset tumours (> 40 years) using data from The Cancer Genome Atlas. We examined mutated genes, mutational load, and types of mutations. We used complementary R packages "deconstructSigs" and "SomaticSignatures" to extract mutational signatures. A recursively partitioned mixture model was used to identify whether combinations of mutational signatures were related to age of onset. RESULTS: Older patients had a higher proportion of mutations in PIK3CA, CDH1, and MAP3K1 genes, while young-onset patients had a higher proportion of mutations in GATA3 and CTNNB1. Mutational load was lower for young-onset tumours, and a higher proportion of these mutations were C > A mutations, but a lower proportion were C > T mutations compared to older-onset tumours. The most common mutational signatures identified in both age groups were signatures 1 and 3 from the COSMIC database. Signatures resembling COSMIC signatures 2 and 13 were observed among both age groups. We identified a class of tumours with a unique combination of signatures that may be associated with young age of onset. CONCLUSIONS: The results of this exploratory study provide some evidence that the mutational landscape and mutational signatures among young-onset breast cancer are different from those of older-onset patients. The characterization of young-onset tumours could provide clues to their etiology which may inform future prevention. Further studies are required to confirm our findings.

Putting benchmarks in their rightful place: The heart of computational biology.

Research in computational biology has given rise to a vast number of methods developed to solve scientific problems. For areas in which many approaches exist, researchers have a hard time deciding which tool to select to address a scientific challenge, as essentially all publications introducing a new method will claim better performance than all others. Not all of these claims can be correct. Equally, for this same reason, developers struggle to demonstrate convincingly that they created a new and superior algorithm or implementation. Moreover, the developer community often has difficulty discerning which new approaches constitute true scientific advances for the field. The obvious answer to this conundrum is to develop benchmarks-meaning standard points of reference that facilitate evaluating the performance of different tools-allowing both users and developers to compare multiple tools in an unbiased fashion.

The lysine acetyltransferase activator Brpf1 governs dentate gyrus development through neural stem cells and progenitors.

Lysine acetylation has recently emerged as an important post-translational modification in diverse organisms, but relatively little is known about its roles in mammalian development and stem cells. Bromodomain- and PHD finger-containing protein 1 (BRPF1) is a multidomain histone binder and a master activator of three lysine acetyltransferases, MOZ, MORF and HBO1, which are also known as KAT6A, KAT6B and KAT7, respectively. While the MOZ and MORF genes are rearranged in leukemia, the MORF gene is also mutated in prostate and other cancers and in four genetic disorders with intellectual disability. Here we show that forebrain-specific inactivation of the mouse Brpf1 gene causes hypoplasia in the dentate gyrus, including underdevelopment of the suprapyramidal blade and complete loss of the infrapyramidal blade. We trace the developmental origin to compromised Sox2+ neural stem cells and Tbr2+ intermediate neuronal progenitors. We further demonstrate that Brpf1 loss deregulates neuronal migration, cell cycle progression and transcriptional control, thereby causing abnormal morphogenesis of the hippocampus. These results link histone binding and acetylation control to hippocampus development and identify an important epigenetic regulator for patterning the dentate gyrus, a brain structure critical for learning, memory and adult neurogenesis.

Cancer modeling and network biology: accelerating toward personalized medicine.

The complexity of cancer progression can manifests itself on at least three scales that can be described using mathematical models, namely microscopic, mesoscopic and macroscopic scales. Multiscale cancer models have proven to be advantageous in this context because they can simultaneously incorporate the many different characteristics and scales of complex diseases such as cancer. This has driven the expansion of more predictive data-driven models, coupled to experimental and clinical data. These models are defining the foundations that facilitate the forthcoming design of patient specific cancer therapy. This should be considered as a great leap toward the era of personalized medicine. Consequently, further improvements in mathematical modeling of cancer will lead to the design of more sophisticated cancer therapy approaches.

Predictive genomics: a cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data.

Tumor genome sequencing leads to documenting thousands of DNA mutations and other genomic alterations. At present, these data cannot be analyzed adequately to aid in the understanding of tumorigenesis and its evolution. Moreover, we have little insight into how to use these data to predict clinical phenotypes and tumor progression to better design patient treatment. To meet these challenges, we discuss a cancer hallmark network framework for modeling genome sequencing data to predict cancer clonal evolution and associated clinical phenotypes. The framework includes: (1) cancer hallmarks that can be represented by a few molecular/signaling networks. 'Network operational signatures' which represent gene regulatory logics/strengths enable to quantify state transitions and measures of hallmark traits. Thus, sets of genomic alterations which are associated with network operational signatures could be linked to the state/measure of hallmark traits. The network operational signature transforms genotypic data (i.e., genomic alterations) to regulatory phenotypic profiles (i.e., regulatory logics/strengths), to cellular phenotypic profiles (i.e., hallmark traits) which lead to clinical phenotypic profiles (i.e., a collection of hallmark traits). Furthermore, the framework considers regulatory logics of the hallmark networks under tumor evolutionary dynamics and therefore also includes: (2) a self-promoting positive feedback loop that is dominated by a genomic instability network and a cell survival/proliferation network is the main driver of tumor clonal evolution. Surrounding tumor stroma and its host immune systems shape the evolutionary paths; (3) cell motility initiating metastasis is a byproduct of the above self-promoting loop activity during tumorigenesis; (4) an emerging hallmark network which triggers genome duplication dominates a feed-forward loop which in turn could act as a rate-limiting step for tumor formation; (5) mutations and other genomic alterations have specific patterns and tissue-specificity, which are driven by aging and other cancer-inducing agents. This framework represents the logics of complex cancer biology as a myriad of phenotypic complexities governed by a limited set of underlying organizing principles. It therefore adds to our understanding of tumor evolution and tumorigenesis, and moreover, potential usefulness of predicting tumors' evolutionary paths and clinical phenotypes. Strategies of using this framework in conjunction with genome sequencing data in an attempt to predict personalized drug targets, drug resistance, and metastasis for cancer patients, as well as cancer risks for healthy individuals are discussed. Accurate prediction of cancer clonal evolution and clinical phenotypes will have substantial impact on timely diagnosis, personalized treatment and personalized prevention of cancer.

Cancer systems biology and modeling: microscopic scale and multiscale approaches.

Cancer has become known as a complex and systematic disease on macroscopic, mesoscopic and microscopic scales. Systems biology employs state-of-the-art computational theories and high-throughput experimental data to model and simulate complex biological procedures such as cancer, which involves genetic and epigenetic, in addition to intracellular and extracellular complex interaction networks. In this paper, different systems biology modeling techniques such as systems of differential equations, stochastic methods, Boolean networks, Petri nets, cellular automata methods and agent-based systems are concisely discussed. We have compared the mentioned formalisms and tried to address the span of applicability they can bear on emerging cancer modeling and simulation approaches. Different scales of cancer modeling, namely, microscopic, mesoscopic and macroscopic scales are explained followed by an illustration of angiogenesis in microscopic scale of the cancer modeling. Then, the modeling of cancer cell proliferation and survival are examined on a microscopic scale and the modeling of multiscale tumor growth is explained along with its advantages.

Deficiency of the chromatin regulator BRPF1 causes abnormal brain development.

Epigenetic mechanisms are important in different neurological disorders, and one such mechanism is histone acetylation. The multivalent chromatin regulator BRPF1 (bromodomain- and plant homeodomain-linked (PHD) zinc finger-containing protein 1) recognizes different epigenetic marks and activates three histone acetyltransferases, so it is both a reader and a co-writer of the epigenetic language. The three histone acetyltransferases are MOZ, MORF, and HBO1, which are also known as lysine acetyltransferase 6A (KAT6A), KAT6B, and KAT7, respectively. The MORF gene is mutated in four neurodevelopmental disorders sharing the characteristic of intellectual disability and frequently displaying callosal agenesis. Here, we report that forebrain-specific inactivation of the mouse Brpf1 gene caused early postnatal lethality, neocortical abnormalities, and partial callosal agenesis. With respect to the control, the mutant forebrain contained fewer Tbr2-positive intermediate neuronal progenitors and displayed aberrant neurogenesis. Molecularly, Brpf1 loss led to decreased transcription of multiple genes, such as Robo3 and Otx1, important for neocortical development. Surprisingly, elevated expression of different Hox genes and various other transcription factors, such as Lhx4, Foxa1, Tbx5, and Twist1, was also observed. These results thus identify an important role of Brpf1 in regulating forebrain development and suggest that it acts as both an activator and a silencer of gene expression in vivo.

The chromatin regulator Brpf1 regulates embryo development and cell proliferation.

With hundreds of chromatin regulators identified in mammals, an emerging issue is how they modulate biological and pathological processes. BRPF1 (bromodomain- and PHD finger-containing protein 1) is a unique chromatin regulator possessing two PHD fingers, one bromodomain and a PWWP domain for recognizing multiple histone modifications. In addition, it binds to the acetyltransferases MOZ, MORF, and HBO1 (also known as KAT6A, KAT6B, and KAT7, respectively) to promote complex formation, restrict substrate specificity, and enhance enzymatic activity. We have recently showed that ablation of the mouse Brpf1 gene causes embryonic lethality at E9.5. Here we present systematic analyses of the mutant animals and demonstrate that the ablation leads to vascular defects in the placenta, yolk sac, and embryo proper, as well as abnormal neural tube closure. At the cellular level, Brpf1 loss inhibits proliferation of embryonic fibroblasts and hematopoietic progenitors. Molecularly, the loss reduces transcription of a ribosomal protein L10 (Rpl10)-like gene and the cell cycle inhibitor p27, and increases expression of the cell-cycle inhibitor p16 and a novel protein homologous to Scp3, a synaptonemal complex protein critical for chromosome association and embryo survival. These results uncover a crucial role of Brpf1 in controlling mouse embryo development and regulating cellular and gene expression programs.

Glycoproteomic comparison of clinical triple-negative and luminal breast tumors.

Triple-negative (TN) breast cancer accounts for ∼ 15% of breast cancers and is characterized by a high likelihood of relapse and a lack of targeted therapies. In contrast, luminal-type tumors that express the estrogen and progesterone receptors (ER+/PR+) and lack expression of human epidermal growth factor receptor 2 (Her2-) are treated with targeted hormonal therapy and carry a better prognosis. To identify potential targets for the development of future therapeutics aimed specifically at TN breast cancers, we have used a hydrazide-based glycoproteomic workflow to compare protein expression in clinical tumors from nine TN (Her2-/ER-/PR-) and nine luminal (Her2-/ER+/PR+) patients. Using a label-free LC-MS based approach, we identified and quantified 2264 proteins. Of these, 90 proteins were more highly expressed and 86 proteins were underexpressed in the TN tumors relative to the luminal tumors. The expression level of four of these potential targets was verified in the original set of tumors by Western blot and correlated well with our mass-spectrometry-based quantification. Furthermore, 30% of the proteins differentially expressed between luminal and TN tumors were validated in a larger cohort of 406 TN and 469 luminal tumors through corresponding differences in their mRNA expression in publically available microarray data. A group of 29 of these differentially expressed proteins was shown to correctly classify 88% of TN and luminal tumors using microarray data of their associated mRNA levels. Interestingly, even within a group of TN breast cancer patients, the expression levels of these same mRNAs were able to significantly predict patient survival, suggesting that these proteins play a role in the aggressiveness seen in TN tumors. This study provides a comprehensive list of potential targets for the development of diagnostic and therapeutic agents specifically aimed at treating TN breast cancer and demonstrates the utility of using publicly available microarray data to further prioritize potential targets.

The human phosphotyrosine signaling network: evolution and hotspots of hijacking in cancer.

Phosphotyrosine (pTyr) signaling, which plays a central role in cell-cell and cell-environment interactions, has been considered to be an evolutionary innovation in multicellular metazoans. However, neither the emergence nor the evolution of the human pTyr signaling system is currently understood. Tyrosine kinase (TK) circuits, each of which consists of a TK writer, a kinase substrate, and a related reader, such as Src homology (SH) 2 domains and pTyr-binding (PTB) domains, comprise the core machinery of the pTyr signaling network. In this study, we analyzed the evolutionary trajectories of 583 literature-derived and 50,000 computationally predicted human TK circuits in 19 representative eukaryotic species and assigned their evolutionary origins. We found that human TK circuits for intracellular pTyr signaling originated largely from primitive organisms, whereas the inter- or extracellular signaling circuits experienced significant expansion in the bilaterian lineage through the "back-wiring" of newly evolved kinases to primitive substrates and SH2/PTB domains. Conversely, the TK circuits that are involved in tissue-specific signaling evolved mainly in vertebrates by the back-wiring of vertebrate substrates to primitive kinases and SH2/PTB domains. Importantly, we found that cancer signaling preferentially employs the pTyr sites, which are linked to more TK circuits. Our work provides insights into the evolutionary paths of the human pTyr signaling circuits and suggests the use of a network approach for cancer intervention through the targeting of key pTyr sites and their associated signaling hubs in the network.

Target of rapamycin signaling regulates metabolism, growth, and life span in Arabidopsis.

Target of Rapamycin (TOR) is a major nutrition and energy sensor that regulates growth and life span in yeast and animals. In plants, growth and life span are intertwined not only with nutrient acquisition from the soil and nutrition generation via photosynthesis but also with their unique modes of development and differentiation. How TOR functions in these processes has not yet been determined. To gain further insights, rapamycin-sensitive transgenic Arabidopsis thaliana lines (BP12) expressing yeast FK506 Binding Protein12 were developed. Inhibition of TOR in BP12 plants by rapamycin resulted in slower overall root, leaf, and shoot growth and development leading to poor nutrient uptake and light energy utilization. Experimental limitation of nutrient availability and light energy supply in wild-type Arabidopsis produced phenotypes observed with TOR knockdown plants, indicating a link between TOR signaling and nutrition/light energy status. Genetic and physiological studies together with RNA sequencing and metabolite analysis of TOR-suppressed lines revealed that TOR regulates development and life span in Arabidopsis by restructuring cell growth, carbon and nitrogen metabolism, gene expression, and rRNA and protein synthesis. Gain- and loss-of-function Ribosomal Protein S6 (RPS6) mutants additionally show that TOR function involves RPS6-mediated nutrition and light-dependent growth and life span in Arabidopsis.