Crosstalk with lung fibroblasts shapes the growth and therapeutic response of mesothelioma cells.

Mesothelioma is an aggressive cancer of the mesothelial layer associated with an extensive fibrotic response. The latter is in large part mediated by cancer-associated fibroblasts which mediate tumour progression and poor prognosis. However, understanding of the crosstalk between cancer cells and fibroblasts in this disease is mostly lacking. Here, using co-cultures of patient-derived mesothelioma cell lines and lung fibroblasts, we demonstrate that fibroblast activation is a self-propagated process producing a fibrotic extracellular matrix (ECM) and triggering drug resistance in mesothelioma cells. Following characterisation of mesothelioma cells/fibroblasts signalling crosstalk, we identify several FDA-approved targeted therapies as far more potent than standard-of-care Cisplatin/Pemetrexed in ECM-embedded co-culture spheroid models. In particular, the SRC family kinase inhibitor, Saracatinib, extends overall survival well beyond standard-of-care in a mesothelioma genetically-engineered mouse model. In short, we lay the foundation for the rational design of novel therapeutic strategies targeting mesothelioma/fibroblast communication for the treatment of mesothelioma patients.

Predicting Response of Triple-Negative Breast Cancer to Neoadjuvant Chemotherapy Using a Deep Convolutional Neural Network-Based Artificial Intelligence Tool.

PURPOSE: Achieving a pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) is associated with improved patient outcomes in triple-negative breast cancer (TNBC). Currently, there are no validated predictive biomarkers for the response to NAC in TNBC. We developed and validated a deep convolutional neural network-based artificial intelligence (AI) model to predict the response of TNBC to NAC. MATERIALS AND METHODS: Whole-slide images (WSIs) of hematoxylin and eosin-stained core biopsies from 165 (pCR in 60 and non-pCR in 105) and 78 (pCR in 31 and non-pCR in 47) patients with TNBC were used to train and validate the model. The model extracts morphometric features from WSIs in an unsupervised manner, thereby generating clusters of morphologically similar patterns. Downstream ranking of clusters provided regions of interest and morphometric scores; a low score close to zero and a high score close to one represented a high or low probability of response to NAC. RESULTS: The predictive ability of AI score for the entire cohort of 78 patients with TNBC ascertained by receiver operating characteristic analysis demonstrated an area under the curve (AUC) of 0.75. The AUC for stages I, II, and III disease were 0.88, 0.73, and 0.74, respectively. Using a cutoff value of 0.35, the positive predictive value of the AI score for pCR was 73.7%, and the negative predictive value was 76.2% for non-pCR patients. CONCLUSION: To our knowledge, this study is the first to demonstrate the use of an AI tool on digitized hematoxylin and eosin-stained tissue images to predict the response to NAC in patients with TNBC with high accuracy. If validated in subsequent studies, these results may serve as an ancillary aid for individualized therapeutic decisions in patients with TNBC.

Comparison of suprainguinal approach with infrainguinal approach of fascia iliaca compartment block for postoperative analgesia.

Background and Aims: Fascia iliaca compartment block (FICB) is increasingly being used for anaesthesia and analgesia of the hip, knee and thigh. It can be administered via two ultrasound-guided approaches, suprainguinal and infrainguinal. This study aimed to compare the analgesic efficacy of the suprainguinal approach of FICB with infrainguinal approach of FICB in patients undergoing above knee orthopaedic surgeries. Methods: In this single-centre, double-blinded randomised trial, 32 patients undergoing above knee lower limb orthopaedic surgery under spinal anaesthesia were randomised into group I (infrainguinal FICB) and S (suprainguinal FICB). They were given ultrasound-guided FICB with 30 mL 0.2% ropivacaine for postoperative analgesia using the respective approaches. Injection tramadol was administered as a rescue analgesic when Numeric Rating Scale (NRS) ≥4. A blinded observer recorded pain score (NRS) from the initiation of the block every 2 h for 24 h. Time to first rescue analgesia, total duration of analgesia and analgesic consumption, and patient satisfaction score were noted. Results: The mean pain scores were comparable between the two groups at all time intervals till 24 h except at 12 h and 20 h, when pain intensity was significantly less in group S than in group I. Total tramadol consumption was also significantly less in group S. There was no significant difference in time to first rescue analgesia and total duration of analgesia between the two groups. At 24 h, the patient satisfaction score was significantly better in group S. Conclusion: Suprainguinal FICB has superior analgesic efficacy over infrainguinal FICB in terms of reduced pain intensity, reduced 24-h tramadol consumption and better patient satisfaction.

Sensitive screening of single nucleotide polymorphisms in cell free DNA for diagnosis of gestational tumours.

Tumours expressing human chorionic gonadotropin (hCG), the majority of which are difficult to biopsy due to their vascularity, have disparate prognoses depending on their origin. As optimal management relies on accurate diagnosis, we aimed to develop a sensitive cell free DNA (cfDNA) assay to non-invasively distinguish between cases of gestational and non-gestational origin. Deep error-corrected Illumina sequencing of 195 common single nucleotide polymorphisms (SNPs) in cfDNA and matched genomic DNA from 36 patients with hCG-secreting tumours (serum hCG 5 to 3,042,881 IU/L) and 7 controls with normal hCG levels (≤4 IU/L) was performed. cfDNA from confirmed gestational tumours with hCG levels ranging from 1497 to 700,855 IU/L had multiple (n ≥ 12) 'non-host' alleles (i.e. alleles of paternal origin). In such cases the non-host fraction of cfDNA ranged from 0.3 to 40.4% and correlated with serum hCG levels. At lower hCG levels the ability to detect non-host cfDNA was variable, with the detection limit dependent on the type of causative pregnancy. Patients with non-gestational tumours were identifiable by the absence of non-host cfDNA, with copy number alterations detectable in the majority of cases. Following validation in a larger cohort, our sensitive assay will enable clinicians to better inform patients, for whom biopsy is inappropriate, of their prognosis and provide optimum management.

ATR and CDK4/6 inhibition target the growth of methotrexate-resistant choriocarcinoma.

Low-risk gestational trophoblastic neoplasia including choriocarcinoma is often effectively treated with Methotrexate (MTX) as a first line therapy. However, MTX resistance (MTX-R) occurs in at least ≈33% of cases. This can sometimes be salvaged with actinomycin-D but often requires more toxic combination chemotherapy. Moreover, additional therapy may be needed and, for high-risk patients, 5% still die from the multidrug-resistant disease. Consequently, new treatments that are less toxic and could reverse MTX-R are needed. Here, we compared the proteome/phosphoproteome of MTX-resistant and sensitive choriocarcinoma cells using quantitative mass-spectrometry to identify therapeutically actionable molecular changes associated with MTX-R. Bioinformatics analysis of the proteomic data identified cell cycle and DNA damage repair as major pathways associated with MTX-R. MTX-R choriocarcinoma cells undergo cell cycle delay in G1 phase that enables them to repair DNA damage more efficiently through non-homologous end joining in an ATR-dependent manner. Increased expression of cyclin-dependent kinase 4 (CDK4) and loss of p16Ink4a in resistant cells suggested that CDK4 inhibition may be a strategy to treat MTX-R choriocarcinoma. Indeed, inhibition of CDK4/6 using genetic silencing or the clinically relevant inhibitor, Palbociclib, induced growth inhibition both in vitro and in an orthotopic in vivo mouse model. Finally, targeting the ATR pathway, genetically or pharmacologically, re-sensitised resistant cells to MTX in vitro and potently prevented the growth of MTX-R tumours in vivo. In short, we identified two novel therapeutic strategies to tackle MTX-R choriocarcinoma that could rapidly be translated into the clinic.

A Novel Artificial Intelligence-Powered Method for Prediction of Early Recurrence of Prostate Cancer After Prostatectomy and Cancer Drivers.

PURPOSE: To develop a novel artificial intelligence (AI)-powered method for the prediction of prostate cancer (PCa) early recurrence and identification of driver regions in PCa of all Gleason Grade Group (GGG). MATERIALS AND METHODS: Deep convolutional neural networks were used to develop the AI model. The AI model was trained on The Cancer Genome Atlas Prostatic Adenocarcinoma (TCGA-PRAD) whole slide images (WSI) and data set (n = 243) to predict 3-year biochemical recurrence after radical prostatectomy (RP) and was subsequently validated on WSI from patients with PCa (n = 173) from the University of Wisconsin-Madison. RESULTS: Our AI-powered platform can extract visual and subvisual morphologic features from WSI to identify driver regions predictive of early recurrence of PCa (regions of interest [ROIs]) after RP. The ROIs were ranked with AI-morphometric scores, which were prognostic for 3-year biochemical recurrence (area under the curve [AUC], 0.78), which is significantly better than the GGG overall (AUC, 0.62). The AI-morphometric scores also showed high accuracy in the prediction of recurrence for low- or intermediate-risk PCa-AUC, 0.76, 0.84, and 0.81 for GGG1, GGG2, and GGG3, respectively. These patients could benefit the most from timely adjuvant therapy after RP. The predictive value of the high-scored ROIs was validated by known PCa biomarkers studied. With this focused biomarker analysis, a potentially new STING pathway-related PCa biomarker-TMEM173-was identified. CONCLUSION: Our study introduces a novel approach for identifying patients with PCa at risk for early recurrence regardless of their GGG status and for identifying cancer drivers for focused evolution-aware novel biomarker discovery.

Development and Validation of an Artificial Intelligence-Powered Platform for Prostate Cancer Grading and Quantification.

Importance: The Gleason grading system has been the most reliable tool for the prognosis of prostate cancer since its development. However, its clinical application remains limited by interobserver variability in grading and quantification, which has negative consequences for risk assessment and clinical management of prostate cancer. Objective: To examine the impact of an artificial intelligence (AI)-assisted approach to prostate cancer grading and quantification. Design, Setting, and Participants: This diagnostic study was conducted at the University of Wisconsin-Madison from August 2, 2017, to December 30, 2019. The study chronologically selected 589 men with biopsy-confirmed prostate cancer who received care in the University of Wisconsin Health System between January 1, 2005, and February 28, 2017. A total of 1000 biopsy slides (1 or 2 slides per patient) were selected and scanned to create digital whole-slide images, which were used to develop and validate a deep convolutional neural network-based AI-powered platform. The whole-slide images were divided into a training set (n = 838) and validation set (n = 162). Three experienced academic urological pathologists (W.H., K.A.I., and R.H., hereinafter referred to as pathologists 1, 2, and 3, respectively) were involved in the validation. Data were collected between December 29, 2018, and December 20, 2019, and analyzed from January 4, 2020, to March 1, 2021. Main Outcomes and Measures: Accuracy of prostate cancer detection by the AI-powered platform and comparison of prostate cancer grading and quantification performed by the 3 pathologists using manual vs AI-assisted methods. Results: Among 589 men with biopsy slides, the mean (SD) age was 63.8 (8.2) years, the mean (SD) prebiopsy prostate-specific antigen level was 10.2 (16.2) ng/mL, and the mean (SD) total cancer volume was 15.4% (20.1%). The AI system was able to distinguish prostate cancer from benign prostatic epithelium and stroma with high accuracy at the patch-pixel level, with an area under the receiver operating characteristic curve of 0.92 (95% CI, 0.88-0.95). The AI system achieved almost perfect agreement with the training pathologist (pathologist 1) in detecting prostate cancer at the patch-pixel level (weighted κ = 0.97; asymptotic 95% CI, 0.96-0.98) and in grading prostate cancer at the slide level (weighted κ = 0.98; asymptotic 95% CI, 0.96-1.00). Use of the AI-assisted method was associated with significant improvements in the concordance of prostate cancer grading and quantification between the 3 pathologists (eg, pathologists 1 and 2: 90.1% agreement using AI-assisted method vs 84.0% agreement using manual method; P < .001) and significantly higher weighted κ values for all pathologists (eg, pathologists 2 and 3: weighted κ = 0.92 [asymptotic 95% CI, 0.90-0.94] for AI-assisted method vs 0.76 [asymptotic 95% CI, 0.71-0.80] for manual method; P < .001) compared with the manual method. Conclusions and Relevance: In this diagnostic study, an AI-powered platform was able to detect, grade, and quantify prostate cancer with high accuracy and efficiency and was associated with significant reductions in interobserver variability. These results suggest that an AI-powered platform could potentially transform histopathological evaluation and improve risk stratification and clinical management of prostate cancer.

The microenvironment and pKa perturbation of aminoacyl-tRNA guided the selection of cationic amino acids.

The proteinogenic lysine (Lys) and arginine (Arg) have multiple methylene groups between α-carbon and the terminal charged centre. Why nature did not select ornithine (Orn), 2,4-diamino butyric acid (Dab) and 2,3-diamino propionic acid (Dpr) with fewer methylene groups in the side chain remains an important question! The propensity of aminoacyl-tRNA (aa-tRNA) model substrates towards self-degradation via intramolecular lactamization was studied using UV spectroscopy and 1H-NMR titration, which showed that Lys and Arg remain stable, and Orn and Dab cyclize to lactam. Hydrophobicity-assisted surface mediated model peptide formation highlighted that the microenvironment and pKa perturbation led to poor regioselectivity (α-amine vs. terminal amine) in Dpr and other non-proteinogenic analogues. The α-selectivity became even poorer in the presence of phosphate, making them ill-suited for peptide synthesis. Superior regioselectivity of the Lys aa-tRNA model substrate suggests that the extra methylene bridge helped nature to separate the microenvironments of the α-amine and ε-amine to synthesize the peptide backbone.

Hydroxy-Porphyrin as an Effective, Endogenous Molecular Clamp during Early Stages of Amyloid Fibrillization.

Amyloid fibril formation of proteins is of great concern in neurodegenerative disease and can be detrimental to the storage and stability of biologics. Recent evidence suggests that insulin fibril formation reduces the efficacy of type II diabetes management and may lead to several complications. To develop anti-amyloidogenic compounds of endogenous origin, we have utilized the hydrogen bond anchoring, π stacking ability of porphyrin, and investigated its role on the inhibition of insulin amyloid formation. We report that hydroxylation and metal removal from the heme moiety yields an excellent inhibitor of insulin fibril formation. Thioflavin T, tyrosine fluorescence, Circular Dichorism (CD) spectroscopy, Field emission scanning electron microscopy (FESEM) and molecular dynamics (MD) simulation studies suggest that hematoporphyrin (HP) having hydrogen bonding ability on both sides is a superior inhibitor compared to hemin and protoporphyrin (PP). Experiments with hen egg white lysozyme (HEWL) amyloid fibril formation also validated the efficacy of endogenous porphyrin based small molecules. Our results will help to decipher a general therapeutic strategy to counter amyloidogenesis.

Repurposed floxacins targeting RSK4 prevent chemoresistance and metastasis in lung and bladder cancer.

Lung and bladder cancers are mostly incurable because of the early development of drug resistance and metastatic dissemination. Hence, improved therapies that tackle these two processes are urgently needed to improve clinical outcome. We have identified RSK4 as a promoter of drug resistance and metastasis in lung and bladder cancer cells. Silencing this kinase, through either RNA interference or CRISPR, sensitized tumor cells to chemotherapy and hindered metastasis in vitro and in vivo in a tail vein injection model. Drug screening revealed several floxacin antibiotics as potent RSK4 activation inhibitors, and trovafloxacin reproduced all effects of RSK4 silencing in vitro and in/ex vivo using lung cancer xenograft and genetically engineered mouse models and bladder tumor explants. Through x-ray structure determination and Markov transient and Deuterium exchange analyses, we identified the allosteric binding site and revealed how this compound blocks RSK4 kinase activation through binding to an allosteric site and mimicking a kinase autoinhibitory mechanism involving the RSK4's hydrophobic motif. Last, we show that patients undergoing chemotherapy and adhering to prophylactic levofloxacin in the large placebo-controlled randomized phase 3 SIGNIFICANT trial had significantly increased (P = 0.048) long-term overall survival times. Hence, we suggest that RSK4 inhibition may represent an effective therapeutic strategy for treating lung and bladder cancer.

A randomised on-line survey exploring how health condition labels affect behavioural intentions.

OBJECTIVES: We examined the effect of 'labels' versus 'descriptions' across four asymptomatic health conditions: pre-diabetes, pre-hypertension, mild hyperlipidaemia, and chronic kidney disease stage 3A, on participants' intentions to pursue further tests. There were four secondary objectives: 1) assessing confidence and satisfaction in their intention to test further; 2) revealing psychological drivers affecting intentions; 3) exploring whether intentions, confidence and satisfaction differ by label vs. description and health condition; and 4) producing a perceptual map of illnesses by label condition. METHODS: Practitioner validated health-related scenarios were used. Two variants of each condition were developed. Participants were recruited through Qualtrics from Australia, Ireland and Canada and randomly assigned two 'labelled' or two 'descriptive' scenarios. RESULTS: There was no significant difference in intentions to test between label and description conditions (95% CI -0.76 to 0.33 points, p = 0.4). Confidence and satisfaction were both positively associated with intentions: regression coefficient (ß) for confidence ß = 0.58 points (95% CI 0.49 to 0.68, p < .001) and for satisfaction 0.67 points (95% CI 0.57 to 0.77, p < .001). Predisposition to seek healthcare (ß = 0.72; 95% CI 0.47 to 0.98), attributing illness to bad luck (ß = -0.16 points; 95% CI -0.3 to -0.02), and concern about the health condition (ß = 0.51; 95% CI 0.38 to 0.65) also significantly predicted intentions. CONCLUSIONS: Unlike studies investigating symptomatic illnesses, the disease label effect on behavioural intentions was not supported suggesting that reducing demand for medical services for borderline cases cannot be achieved by labelling. The average intention to test score was higher in this sample than previous symptomatic health-related studies and there was a positive relationship between increased intentions and confidence/satisfaction in one's decision. Exploratory insights suggested perceptions of the four labelled asymptomatic illnesses all shifted toward greater levels of dread and concern compared to their respective description condition. TRIAL REGISTRATION: ACTRN12618000392268.

Effect of a low-dose dexmedetomidine infusion on intraoperative hemodynamics, anesthetic requirements and recovery profile in patients undergoing lumbar spine surgery.

BACKGROUND AND AIMS: Dexmedetomidine has been used as an anesthetic adjuvant; however, hypotension is a concern especially in prone patients. The aim of the study was to evaluate the effect of a low-dose dexmedetomidine infusion on intraoperative hemodynamics, blood loss, anesthetic requirements, and recovery profile in patients undergoing lumbar spine surgery in the prone position. MATERIAL AND METHODS: The study was conducted in a randomized double-blinded manner in 60 patients scheduled for one- or two-level lumbar laminectomy. After administration of general anesthesia, patients were placed in prone position and allocated to either of two groups of 30 patients each. Patients in Group A received dexmedetomidine infusion at the rate of 0.3 µg kg-1 hr-1, whereas, group B patients received a saline infusion. The depth of anesthesia was guided by Bispectral index (BIS) monitoring, maintaining BIS between 40 and 60. RESULTS: The demographic profile and duration of surgery in both groups were similar. Mean heart rate was statistically similar in both the groups. Mean blood pressure was lower in group A, though the difference was significant only for the initial 30 min. The mean end-tidal sevoflurane requirement in group A was significantly less than that in group B (P = 0.003). Patients in group A had better recovery profile with mean emergence, extubation, and recovery times of 8.08 ± 3.48 min, 9.37 ± 3.64 min, and 11.65 ± 4.03 min, respectively, as compared with 11.27 ± 3.05 min, 12.24 ± 2.39 min, and 14.90 ± 2.63 min, respectively, in group B (P < 0.001). Mean intraoperative blood loss in group A of 263.47 ± 58.66 mL was significantly lower than 347.67 ± 72.90 ml in group B (P = 0.0001). CONCLUSION: Group A patients had stable hemodynamic parameters, reduced intraoperative blood loss, less anesthetic requirement, and could be extubated earlier as compared with group B patients.

Targeting autophagy sensitises lung cancer cells to Src family kinase inhibitors.

Lung cancer is the main cancer killer in both men and women, mostly due to the rapid development of drug resistant metastatic disease. Here, we evaluate the potential involvement of SRC family kinases (SFK) in lung cancer biology and assess the possible benefits of their inhibition as a therapeutic approach. We demonstrated that various SRC family members, including LYN and LCK, normally expressed solely in hematopoietic cells and neural tissues, are overexpressed and activated in a panel of SCLC and NSCLC cell lines. This was clinically relevant as LYN and FYN are also overexpressed in lung cancer clinical specimens. Moreover, LYN overexpression correlated with decreased patient survival on univariate and multivariate analysis. Dasatinib (BMS-354825), a SRC/ABL inhibitor, effectively blocked SFK activation at nanomolar concentrations which correlated with a significant decrease in cell numbers of multiple lung cancer cell lines. This effect was matched by a decrease in DNA synthesis, but only moderate induction of apoptosis. Indeed, dasatinib as well as PP2, another SFK inhibitor, strongly induced autophagy that likely prevented apoptosis. However, inhibition of this autophagic response induced robust apoptosis and sensitised lung cancer cells to dasatinib in vitro and in vivo. Our results provide an explanation for why dasatinib failed in NSCLC clinical trials. Furthermore, our data suggest that combining SFK inhibitors with autophagy inhibitors could provide a novel therapeutic approach in this disease.

Emerging roles of hnRNPA1 in modulating malignant transformation.

Heterogeneous nuclear ribonucleoproteins (hnRNPs) are RNA-binding proteins associated with complex and diverse biological processes such as processing of heterogeneous nuclear RNAs (hnRNAs) into mature mRNAs, RNA splicing, transactivation of gene expression, and modulation of protein translation. hnRNPA1 is the most abundant and ubiquitously expressed member of this protein family and has been shown to be involved in multiple molecular events driving malignant transformation. In addition to selective mRNA splicing events promoting expression of specific protein variants, hnRNPA1 regulates the gene expression and translation of several key players associated with tumorigenesis and cancer progression. Here, we will summarize our current knowledge of the involvement of hnRNPA1 in cancer, including its roles in regulating cell proliferation, invasiveness, metabolism, adaptation to stress and immortalization. WIREs RNA 2017, 8:e1431. doi: 10.1002/wrna.1431 For further resources related to this article, please visit the WIREs website.

Oncogene-Selective Sensitivity to Synchronous Cell Death following Modulation of the Amino Acid Nutrient Cystine.

Cancer cells reprogram their metabolism, altering both uptake and utilization of extracellular nutrients. We individually depleted amino acid nutrients from isogenic cells expressing commonly activated oncogenes to identify correspondences between nutrient supply and viability. In HME (human mammary epithelial) cells, deprivation of cystine led to increased cell death in cells expressing an activated epidermal growth factor receptor (EGFR) mutant. Cell death occurred via synchronous ferroptosis, with generation of reactive oxygen species (ROS). Hydrogen peroxide promoted cell death, as both catalase and inhibition of NADPH oxidase 4 (NOX4) blocked ferroptosis. Blockade of EGFR or mitogen-activated protein kinase (MAPK) signaling similarly protected cells from ferroptosis, whereas treatment of xenografts derived from EGFR mutant non-small-cell lung cancer (NSCLC) with a cystine-depleting enzyme inhibited tumor growth in mice. Collectively, our results identify a potentially exploitable sensitization of some EGFR/MAPK-driven tumors to ferroptosis following cystine depletion.

Decreased glutathione biosynthesis contributes to EGFR T790M-driven erlotinib resistance in non-small cell lung cancer.

Epidermal growth factor receptor (EGFR) inhibitors such as erlotinib are novel effective agents in the treatment of EGFR-driven lung cancer, but their clinical impact is often impaired by acquired drug resistance through the secondary T790M EGFR mutation. To overcome this problem, we analysed the metabonomic differences between two independent pairs of erlotinib-sensitive/resistant cells and discovered that glutathione (GSH) levels were significantly reduced in T790M EGFR cells. We also found that increasing GSH levels in erlotinib-resistant cells re-sensitised them, whereas reducing GSH levels in erlotinib-sensitive cells made them resistant. Decreased transcription of the GSH-synthesising enzymes (GCLC and GSS) due to the inhibition of NRF2 was responsible for low GSH levels in resistant cells that was directly linked to the T790M mutation. T790M EGFR clinical samples also showed decreased expression of these key enzymes; increasing intra-tumoural GSH levels with a small-molecule GST inhibitor re-sensitised resistant tumours to erlotinib in mice. Thus, we identified a new resistance pathway controlled by EGFR T790M and a therapeutic strategy to tackle this problem in the clinic.

hnRNPA1 couples nuclear export and translation of specific mRNAs downstream of FGF-2/S6K2 signalling.

The increased cap-independent translation of anti-apoptotic proteins is involved in the development of drug resistance in lung cancer but signalling events regulating this are poorly understood. Fibroblast growth factor 2 (FGF-2) signalling-induced S6 kinase 2 (S6K2) activation is necessary, but the downstream mediator(s) coupling this kinase to the translational response is unknown. Here, we show that S6K2 binds and phosphorylates hnRNPA1 on novel Ser4/6 sites, increasing its association with BCL-XL and XIAP mRNAs to promote their nuclear export. In the cytoplasm, phosphoS4/6-hnRNPA1 dissociates from these mRNAs de-repressing their IRES-mediated translation. This correlates with the phosphorylation-dependent association of hnRNPA1 with 14-3-3 leading to hnRNPA1 sumoylation on K183 and its re-import into the nucleus. A non-phosphorylatible, S4/6A mutant prevented these processes, hindering the pro-survival activity of FGF-2/S6K2 signalling. Interestingly, immunohistochemical staining of lung and breast cancer tissue samples demonstrated that increased S6K2 expression correlates with decreased cytoplasmic hnRNPA1 and increased BCL-XL expression. In short, phosphorylation on novel N-term sites of hnRNPA1 promotes translation of anti-apoptotic proteins and is indispensable for the pro-survival effects of FGF-2.

Single cell genome analysis of an uncultured heterotrophic stramenopile.

A broad swath of eukaryotic microbial biodiversity cannot be cultivated in the lab and is therefore inaccessible to conventional genome-wide comparative methods. One promising approach to study these lineages is single cell genomics (SCG), whereby an individual cell is captured from nature and genome data are produced from the amplified total DNA. Here we tested the efficacy of SCG to generate a draft genome assembly from a single sample, in this case a cell belonging to the broadly distributed MAST-4 uncultured marine stramenopiles. Using de novo gene prediction, we identified 6,996 protein-encoding genes in the MAST-4 genome. This genetic inventory was sufficient to place the cell within the ToL using multigene phylogenetics and provided preliminary insights into the complex evolutionary history of horizontal gene transfer (HGT) in the MAST-4 lineage.

Turtle: identifying frequent k-mers with cache-efficient algorithms.

MOTIVATION: Counting the frequencies of k-mers in read libraries is often a first step in the analysis of high-throughput sequencing data. Infrequent k-mers are assumed to be a result of sequencing errors. The frequent k-mers constitute a reduced but error-free representation of the experiment, which can inform read error correction or serve as the input to de novo assembly methods. Ideally, the memory requirement for counting should be linear in the number of frequent k-mers and not in the, typically much larger, total number of k-mers in the read library. RESULTS: We present a novel method that balances time, space and accuracy requirements to efficiently extract frequent k-mers even for high-coverage libraries and large genomes such as human. Our method is designed to minimize cache misses in a cache-efficient manner by using a pattern-blocked Bloom filter to remove infrequent k-mers from consideration in combination with a novel sort-and-compact scheme, instead of a hash, for the actual counting. Although this increases theoretical complexity, the savings in cache misses reduce the empirical running times. A variant of method can resort to a counting Bloom filter for even larger savings in memory at the expense of false-negative rates in addition to the false-positive rates common to all Bloom filter-based approaches. A comparison with the state-of-the-art shows reduced memory requirements and running times. AVAILABILITY AND IMPLEMENTATION: The tools are freely available for download at http://bioinformatics.rutgers.edu/Software/Turtle and http://figshare.com/articles/Turtle/791582.