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Summary The Columbia Cancer Target Discovery and Development (CTD2) Center is developing PANACEA, a resource comprising dose-responses and RNA sequencing (RNA-seq) profiles of 25 cell lines perturbed with ∼400 clinical oncology drugs, to study a tumor-specific drug mechanism of action. Here, this resource serves as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for de novo drug polypharmacology predictions. Dose-response and perturbational profiles for 32 kinase inhibitors are provided to 21 teams who are blind to the identity of the compounds. The teams are asked to predict high-affinity binding targets of each compound among ∼1,300 targets cataloged in DrugBank. The best performing methods leverage gene expression profile similarity analysis as well as deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessments of drug mechanisms of action., Graphical abstract, Highlights • Drug-perturbed RNA sequencing data can be used to identify drug targets • Technology-based drug-target definitions often subsume literature definitions • Literature and screening datasets provide complementary information on drug mechanisms, Douglass et al. report the results of a crowdsourced challenge to develop machine-learning algorithms that use drug-perturbed transcriptome data to rapidly predict drug targets on a proteomic scale. Winning methods effectively predicted off-target binding of clinical kinase inhibitors and clarified disparate literature on these drugs’ mechanisms of action.

Although the development of hematopoietic stem cells (HSC) has been studied in great detail, their heterogeneity and relationships to different cell lineages remain incompletely understood. Moreover, the role of Vascular Adhesion Protein-1 in bone marrow hematopoiesis has remained unknown. Here we show that VAP-1, an adhesin and a primary amine oxidase producing hydrogen peroxide, is expressed on a subset of human HSC and bone marrow vasculature forming a hematogenic niche. Bulk and single-cell RNAseq analyses reveal that VAP-1+ HSC represent a transcriptionally unique small subset of differentiated and proliferating HSC, while VAP-1− HSC are the most primitive HSC. VAP-1 generated hydrogen peroxide acts via the p53 signaling pathway to regulate HSC proliferation. HSC expansion and differentiation into colony-forming units are enhanced by inhibition of VAP-1. Contribution of VAP-1 to HSC proliferation was confirmed with mice deficient of VAP-1, mice expressing mutated VAP-1 and using an enzyme inhibitor. In conclusion, VAP-1 expression allows the characterization and prospective isolation of a new subset of human HSC. Since VAP-1 serves as a check point-like inhibitor in HSC differentiation, the use of VAP-1 inhibitors enables the expansion of HSC. Supplementary Information The online version contains supplementary material available at 10.1007/s00018-021-03977-6.

Analysis of dose-response data is an important step in many scientific disciplines, including but not limited to pharmacology, toxicology, and epidemiology. The R package drda is designed to facilitate the analysis of dose-response data by implementing efficient and accurate functions with a familiar interface. With drda, it is possible to fit models by the method of least squares, perform goodness of fit tests, and conduct model selection. Compared to other similar packages, drda provides, in general, more accurate estimates in the least-squares sense. This result is achieved by a smart choice of the starting point in the optimization algorithm and by implementing the Newton method with a trust region with analytical gradients and Hessian matrices. In this article, drda is presented through the description of its methodological components and examples of its user-friendly functions. Performance is finally evaluated using a real, large-scale drug sensitivity screening dataset.

Combinatorial therapies have been recently proposed to improve the efficacy of anticancer treatment. The SynergyFinder R package is a software used to analyze pre-clinical drug combination datasets. Here, we report the major updates to the SynergyFinder R package for improved interpretation and annotation of drug combination screening results. Unlike the existing implementations, the updated SynergyFinder R package includes five main innovations. (1) We extend the mathematical models to higher-order drug combination data analysis and implement dimension reduction techniques for visualizing the synergy landscape. (2) We provide a statistical analysis of drug combination synergy and sensitivity with confidence intervals and P values. (3) We incorporate a synergy barometer to harmonize multiple synergy scoring methods to provide a consensus metric for synergy. (4) We evaluate drug combination synergy and sensitivity to provide an unbiased interpretation of the clinical potential. (5) We enable fast annotation of drugs and cell lines, including their chemical and target information. These annotations will improve the interpretation of the mechanisms of action of drug combinations. To facilitate the use of the R package within the drug discovery community, we also provide a web server at www.synergyfinderplus.org as a user-friendly interface to enable a more flexible and versatile analysis of drug combination data.

The simple (linear) birth-and-death process is a widely used stochastic model for describing the dynamics of a population. When the process is observed discretely over time, despite the large amount of literature on the subject, little is known about formal estimator properties. Here we will show that its application to observed data is further complicated by the fact that numerical evaluation of the well-known transition probability is an ill-conditioned problem. To overcome this difficulty we will rewrite the transition probability in terms of a Gaussian hypergeometric function and subsequently obtain a three-term recurrence relation for its accurate evaluation. We will also study the properties of the hypergeometric function as a solution to the three-term recurrence relation. We will then provide formulas for the gradient and Hessian of the log-likelihood function and conclude the article by applying our methods for numerically computing maximum likelihood estimates in both simulated and real dataset.

Regulation of the cellular metabolism is now recognized as a crucial mechanism for the homeostasis of innate and adaptive immune cells upon diverse extracellular stimuli. Macrophages, for instance, increase glycolysis upon stimulation with pathogen-associated molecular patterns (PAMPs). Conceivably, pathogens also counteract these metabolic changes for their own survival in the host. However, despite this dynamic interplay in host-pathogen interactions, the role of immunometabolism in the context of intracellular bacterial infections is still unclear. Here, employing unbiased metabolomic and transcriptomic approaches, we investigated the role of metabolic adaptations of macrophages upon Salmonella enterica serovar Typhimurium (S. Typhimurium) infections. Importantly, our results suggested that S. Typhimurium abrogates glycolysis and its modulators such as insulin-signaling to impair macrophage defense. Mechanistically, glycolytic enzyme aldolase A is critical for v-ATPase assembly and the acidification of phagosomes upon S. Typhimurium infection, and impairment in the glycolytic machinery eventually leads to decreased bacterial clearance and antigen presentation in macrophages. Collectively, our results highlight a vital molecular link between metabolic adaptation and phagosome maturation in macrophages, which is targeted by S. Typhimurium to evade cell-autonomous defense.

The Columbia Cancer Target Discovery and Development (CTD2) Center has developed PANACEA (PANcancer Analysis of Chemical Entity Activity), a collection of dose-response curves and perturbational profiles for 400 clinical oncology drugs in cell lines selected to optimally represent 19 cancer subtypes. This resource, developed to study tumor-specific drug mechanism of action, was instrumental in hosting a DREAM Challenge to assess computational models for de novo drug polypharmacology prediction. Dose-response and perturbational profiles for 32 kinase inhibitors were provided to 21 participating teams who were asked to predict high-affinity binding target among 255 possible protein kinases. Best performing methods leveraged both gene expression profile similarity analysis, and deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessment of context-specific drug mechanism of action.

SUMMARYThe Columbia Cancer Target Discovery and Development (CTD2) Center has developed PANACEA (PANcancer Analysis of Chemical Entity Activity), a collection of dose-response curves and perturbational profiles for 400 clinical oncology drugs in cell lines selected to optimally represent 19 cancer subtypes. This resource, developed to study tumor-specific drug mechanism of action, was instrumental in hosting a DREAM Challenge to assess computational models for de novo drug polypharmacology prediction. Dose-response and perturbational profiles for 32 kinase inhibitors were provided to 21 participating teams, who did not know the identity or nature of the compounds, and they were asked to predict high-affinity binding among ~1,300 possible protein targets. Best performing methods leveraged both gene expression profile similarity analysis, and deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessment of context-specific drug mechanism of action.

Hydroxysteroid 17-beta dehydrogenase 12 (HSD17B12) is suggested to be involved in the elongation of very long chain fatty acids. Previously, we have shown a pivotal role for the enzyme during mouse development. In the present study we generated a conditional Hsd17b12 knockout (HSD17B12cKO) mouse model by breeding mice homozygous for a floxed Hsd17b12 allele with mice expressing the tamoxifen-inducible Cre recombinase at the ROSA26 locus. Gene inactivation was induced by administering tamoxifen to adult mice. The gene inactivation led to a 20% loss of body weight within six days, associated with drastic reduction in both white (83% males, 75% females) and brown (65% males, 60% females) fat, likely due to markedly reduced food and water intake. Furthermore, the knockout mice showed sickness behavior and signs of liver toxicity, specifically microvesicular hepatic steatosis and increased serum alanine aminotransferase (4.6-fold in males, 7.7-fold in females). The hepatic changes were more pronounced in females than males. Pro-inflammatory cytokines, such as interleukin 6 (IL-6), IL-17 and granulocyte-colony stimulating factor were increased in the HSD17B12cKO mice indicating inflammatory response. Serum lipidomics study showed an increase in the amount of dihydroceramides, despite the dramatic overall loss of lipids. In line with the proposed role for HSD17B12 in the fatty acid elongation, we observed accumulation of ceramides, dihydroceramides, hexosylceramides and lactosylceramides with shorter than 18-carbon fatty acid side chains in the serum. The results indicate that HSD17B12 is essential for proper lipid homeostasis, and HSD17B12 deficiency rapidly results in fatal systemic inflammation and lipolysis in adult mice.

NAD(+) is a redox-active metabolite, the depletion of which has been proposed to promote aging and degenerative diseases in rodents. However, whether NAD(+) depletion occurs in patients with degenerative disorders and whether NAD(+) repletion improves their symptoms has remained open. Here, we report systemic NAD(+) deficiency in adult-onset mitochondrial myopathy patients. We administered an increasing dose of NAD(+) booster niacin, a vitamin B3 form (to 750-1,000 mg/day; clinicaltrials.gov NCT03973203) for patients and their matched controls for 10 or 4 months, respectively. Blood NAD(+) increased in all subjects, up to 8-fold, and muscle-NAD(+) of patients reached the level of their controls. Some patients showed anemia tendency, while muscle strength and mitochondrial biogenesis increased in all subjects. In patients, muscle metabolome shifted toward controls and liver fat decreased even 50%. Our evidence indicates that blood analysis is useful in identifying NAD(+) deficiency and points niacin to be an efficient NAD(+) booster for treating mitochondrial myopathy.

Regulation of the cellular metabolism is now recognized as a crucial mechanism for the homeostasis of innate and adaptive immune cells upon diverse extracellular stimuli. Macrophages, for instance, increase glycolysis upon stimulation with pathogen-associated molecular patterns (PAMPs). Conceivably, pathogens also counteract these metabolic changes for their own survival in the host. However, despite this dynamic interplay in host-pathogen interactions, the role of immunometabolism in the context of intracellular bacterial infections is still unclear. Here, employing unbiased metabolomic and transcriptomic approaches, we investigated the role of metabolic adaptations of macrophages upon Salmonella enterica serovar Typhimurium (S. Typhimurium) infections. Importantly, our results suggested that S. Typhimurium abrogates glycolysis and its modulators such as insulin-signaling to impair macrophage defense. Mechanistically, glycolytic enzyme aldolase A is critical for v-ATPase assembly and the acidification of phagosomes upon S. Typhimurium infection, and impairment in the glycolytic machinery eventually leads to decreased bacterial clearance and antigen presentation in macrophages. Collectively, our results highlight a vital molecular link between metabolic adaptation and phagosome maturation in macrophages, which is targeted by S. Typhimurium to evade cell-autonomous defense.

CLUH binds mRNAs implicated in intermediate metabolism and oxidative phosphorylation, but the physiological and molecular significance of these interactions is unclear. Schatton et al. use new constitutive and liver-specific Cluh knockouts to define the function of CLUH in catabolic and energy-converting pathways as a regulator of the translation and stability of target mRNAs., Mitochondria are essential organelles that host crucial metabolic pathways and produce adenosine triphosphate. The mitochondrial proteome is heterogeneous among tissues and can dynamically change in response to different metabolic conditions. Although the transcriptional programs that govern mitochondrial biogenesis and respiratory function are well known, posttranscriptional regulatory mechanisms remain unclear. In this study, we show that the cytosolic RNA-binding protein clustered mitochondria homologue (CLUH) regulates the expression of a mitochondrial protein network supporting key metabolic programs required under nutrient deprivation. CLUH exerts its function by controlling the stability and translation of target messenger RNAs. In the absence of Cluh, mitochondria are severely depleted of crucial enzymes involved in catabolic energy-converting pathways. CLUH preserves oxidative mitochondrial function and glucose homeostasis, thus preventing death at the fetal–neonatal transition. In the adult liver, CLUH ensures maximal respiration capacity and the metabolic response to starvation. Our results shed new light on the posttranscriptional mechanisms controlling the expression of mitochondrial proteins and suggest novel strategies to tailor mitochondrial function to physiological and pathological conditions.

Background Probing genetic dependencies of cancer cells can improve our understanding of tumour development and progression, as well as identify potential drug targets. CRISPR-Cas9-based and shRNA-based genetic screening are commonly utilized to identify essential genes that affect cancer growth. However, systematic methods leveraging these genetic screening techniques to derive consensus cancer dependency maps for individual cancer cell lines are lacking. Finding In this work, we first explored the CRISPR-Cas9 and shRNA gene essentiality profiles in 42 cancer cell lines representing 10 cancer types. We observed limited consistency between the essentiality profiles of these two screens at the genome scale. To improve consensus on the cancer dependence map, we developed a computational model called combined essentiality score (CES) to integrate the genetic essentiality profiles from CRISPR-Cas9 and shRNA screens, while accounting for the molecular features of the genes. We found that the CES method outperformed the existing gene essentiality scoring approaches in terms of ability to detect cancer essential genes. We further demonstrated the power of the CES method in adjusting for screen-specific biases and predicting genetic dependencies in individual cancer cell lines. Interpretation Systematic comparison of the CRISPR-Cas9 and shRNA gene essentiality profiles showed the limitation of relying on a single technique to identify cancer essential genes. The CES method provides an integrated framework to leverage both genetic screening techniques as well as molecular feature data to determine gene essentiality more accurately for cancer cells.

NAD

Drug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of drug resistance. However, discovery of synergistic and effective drug combinations has been a laborious and often serendipitous process. In recent years, identification of combination therapies has been accelerated due to the advances in high-throughput drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal (https://drugcomb.fimm.fi) where the results of drug combination screening studies are accumulated, standardized and harmonized. Through the data portal, we provided the web server to analyze and visualize users’ own drug combination screening data. The users have an option to upload their data to DrugComb, as part of a crowdsourcing data curation effort. To initiate the data repository, we collected 437,932 drug combinations tested on a variety of cancer cell lines. We showed that linear regression approaches, when considering chemical fingerprints as predictors, have the potential to achieve high accuracy of predicting the sensitivity and synergy of drug combinations. All the data and informatics tools are freely available in DrugComb to enable a more efficient utilization of data resources for future drug combination discovery.

High-throughput drug screening has facilitated the discovery of drug combinations in cancer. Many existing studies adopted a full matrix design, aiming for the characterization of drug pair effects for cancer cells. However, the full matrix design may be suboptimal as it requires a drug pair to be combined at multiple concentrations in a full factorial manner. Furthermore, many of the computational tools assess only the synergy but not the sensitivity of drug combinations, which might lead to false positive discoveries. We proposed a novel cross design to enable a more cost-effective and simultaneous testing of drug combination sensitivity and synergy. We developed a drug combination sensitivity score (CSS) to determine the sensitivity of a drug pair, and showed that the CSS is highly reproducible between the replicates and thus supported its usage as a robust metric. We further showed that CSS can be predicted using machine learning approaches which determined the top pharmaco-features to cluster cancer cell lines based on their drug combination sensitivity profiles. To assess the degree of drug interactions using the cross design, we developed an S synergy score based on the difference between the drug combination and the single drug dose-response curves. We showed that the S score is able to detect true synergistic and antagonistic drug combinations at an accuracy level comparable to that using the full matrix design. Taken together, we showed that the cross design coupled with the CSS sensitivity and S synergy scoring methods may provide a robust and accurate characterization of both drug combination sensitivity and synergy levels, with minimal experimental materials required. Our experimental-computational approach could be utilized as an efficient pipeline for improving the discovery rate in high-throughput drug combination screening, particularly for primary patient samples which are difficult to obtain., Author summary Cancer is one of the main causes of death worldwide. Although new treatment strategies have been achieved, they still have limited efficacy as cancer cells can easily develop drug resistance. To achieve more sustainable therapies to treat cancer, we need multi-targeted drug combinations that can inhibit cancer cells more effectively and synergistically. However, the increasing number of possible drug combinations makes a full matrix design unfeasible, even with automated drug screening instruments. Therefore, we proposed a novel cross design to access drug combinations more efficiently. We further developed a drug combination sensitivity score (CSS) that is tailored for the cross design to quantify the efficacy of a drug combination. Using public datasets, we showed that the CSS is a robust metric and highly predictive with an accuracy comparable to the experimental replicates. We also developed a CSS-based synergy score to assess the degree of drug interaction and showed its capability to correctly identify synergistic and antagonistic drug combinations. Taken together, we showed that the cross design and its scoring methods allow a more systematic and cost-effective evaluation of drug combinations. The proposed experimental and computational techniques are expected to be widely applicable in the field of drug combination discovery.

Drug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of drug resistance. However, discovery of synergistic and effective drug combinations has been a laborious and often serendipitous process. In recent years, identification of combination therapies has been accelerated due to the advances in high-throughput drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal called DrugComb (https://drugcomb.fimm.fi) where the results of drug combination screening studies are accumulated, standardized and harmonized. Through the data portal, we provided a web server to analyze and visualize users’ own drug combination screening data. The users can also effectively participate a crowdsourcing data curation effect by depositing their data at DrugComb. To initiate the data repository, we collected 437 932 drug combinations tested on a variety of cancer cell lines. We showed that linear regression approaches, when considering chemical fingerprints as predictors, have the potential to achieve high accuracy of predicting the sensitivity of drug combinations. All the data and informatics tools are freely available in DrugComb to enable a more efficient utilization of data resources for future drug combination discovery.

Swine influenza A viruses (swIAVs) can play a crucial role in the generation of new human pandemic viruses. In this study, in-depth passive surveillance comprising nearly 2,500 European swine holdings and more than 18,000 individual samples identified a year-round presence of up to four major swIAV lineages on more than 50% of farms surveilled. Phylogenetic analyses show that intensive reassortment with human pandemic A(H1N1)/2009 (H1pdm) virus produced an expanding and novel repertoire of at least 31 distinct swIAV genotypes and 12 distinct hemagglutinin/neuraminidase combinations with largely unknown consequences for virulence and host tropism. Several viral isolates were resistant to the human antiviral MxA protein, a prerequisite for zoonotic transmission and stable introduction into human populations. A pronounced antigenic variation was noted in swIAV, and several H1pdm lineages antigenically distinct from current seasonal human H1pdm co-circulate in swine. Thus, European swine populations represent reservoirs for emerging IAV strains with zoonotic and, possibly, pre-pandemic potential.

Background Probing genetic dependencies of cancer cells can improve our understanding of tumour development and progression, as well as identify potential drug targets. CRISPR-Cas9-based and shRNA-based genetic screening are commonly utilized to identify essential genes that affect cancer growth. However, systematic methods leveraging these genetic screening techniques to derive consensus cancer dependency maps for individual cancer cell lines are lacking. Finding In this work, we first explored the CRISPR-Cas9 and shRNA gene essentiality profiles in 42 cancer cell lines representing 10 cancer types. We observed limited consistency between the essentiality profiles of these two screens at the genome scale. To improve consensus on the cancer dependence map, we developed a computational model called combined essentiality score (CES) to integrate the genetic essentiality profiles from CRISPR-Cas9 and shRNA screens, while accounting for the molecular features of the genes. We found that the CES method outperformed the existing gene essentiality scoring approaches in terms of ability to detect cancer essential genes. We further demonstrated the power of the CES method in adjusting for screen-specific biases and predicting genetic dependencies in individual cancer cell lines. Interpretation Systematic comparison of the CRISPR-Cas9 and shRNA gene essentiality profiles showed the limitation of relying on a single technique to identify cancer essential genes. The CES method provides an integrated framework to leverage both genetic screening techniques as well as molecular feature data to determine gene essentiality more accurately for cancer cells.

Pancreatic cystic neoplasms (PCNs) are a highly prevalent disease of the pancreas. Among PCNs, Intraductal Papillary Mucinous Neoplasms (IPMNs) are common lesions that may progress from low-grade dysplasia (LGD) through high-grade dysplasia (HGD) to invasive cancer. Accurate discrimination of IPMN-associated neoplastic grade is an unmet clinical need. Targeted (semi)quantitative analysis of 100 metabolites and >1000 lipid species were performed on peri-operative pancreatic cyst fluid and pre-operative plasma from IPMN and serous cystic neoplasm (SCN) patients in a pancreas resection cohort (n = 35). Profiles were correlated against histological diagnosis and clinical parameters after correction for confounding factors. Integrated data modeling was used for group classification and selection of the best explanatory molecules. Over 1000 different compounds were identified in plasma and cyst fluid. IPMN profiles showed significant lipid pathway alterations compared to SCN. Integrated data modeling discriminated between IPMN and SCN with 100% accuracy and distinguished IPMN LGD or IPMN HGD and invasive cancer with up to 90.06% accuracy. Free fatty acids, ceramides, and triacylglycerol classes in plasma correlated with circulating levels of CA19-9, albumin and bilirubin. Integrated metabolomic and lipidomic analysis of plasma or cyst fluid can improve discrimination of IPMN from SCN and within PMNs predict the grade of dysplasia.

The use of metabolomics profiling to understand the metabolism under different physiological states has increased in recent years, which created the need for robust analytical platforms. Here, we present a validated method for targeted and semiquantitative analysis of 102 polar metabolites that cover major metabolic pathways from 24 classes in a single 17.5-min assay. The method has been optimized for a wide range of biological matrices from various organisms, and involves automated sample preparation and data processing using an inhouse developed R-package. To ensure reliability, the method was validated for accuracy, precision, selectivity, specificity, linearity, recovery, and stability according to European Medicines Agency guidelines. We demonstrated an excellent repeatability of retention times (CV &lt, 4%), calibration curves (R2 &ge, 0.980) in their respective wide dynamic concentration ranges (CV &lt, 3%), and concentrations (CV &lt, 25%) of quality control samples interspersed within 25 batches analyzed over a period of one year. The robustness was demonstrated through a high correlation between metabolite concentrations measured using our method and the NIST reference values (R2 = 0.967), including cross-platform comparability against the BIOCRATES AbsoluteIDQp180 kit (R2 = 0.975) and NMR analyses (R2 = 0.884). We have shown that our method can be successfully applied in many biomedical research fields and clinical trials, including epidemiological studies for biomarker discovery. In summary, a thorough validation demonstrated that our method is reproducible, robust, reliable, and suitable for metabolomics studies.

The use of metabolomics profiling to understand metabolism under different physiological states has increased in recent years, which created the need for robust analytical platforms. Here, we present a validated method for targeted and semi-quantitative analysis of 102 polar metabolites that covers major metabolic pathways from 24 classes in a single 17.5-min assay. The method has been optimized for a wide range of biological matrices from various organisms, and involves automated sample preparation, and data processing using in-house developed R package. To ensure reliability, the method was validated for accuracy, precision, selectivity, specificity, linearity, recovery, and stability according to European Medicines Agency guidelines. We demonstrated excellent repeatability of the retention times (CV2≥0.980) in their respective wide dynamic concentration ranges (CV2=0.967), including cross-platform comparability against the BIOCRATES AbsoluteIDQp180 kit (R2=0.975) and NMR analyses (R2=0.884). We have shown that our method can be successfully applied in many biomedical research fields and clinical trials, including epidemiological studies for biomarker discovery. In summary, a thorough validation demonstrated that our method is reproducible, robust, reliable, and suitable for metabolomics studies.

Probing the genetic dependencies of cancer cells helps understand the tumor biology and identify potential drug targets. RNAi-based shRNA and CRISPR/Cas9-based sgRNA have been commonly utilized in functional genetic screens to identify essential genes affecting growth rates in cancer cell lines. However, questions remain whether the gene essentiality profiles determined using these two technologies are comparable. In the present study, we collected 42 cell lines representing a variety of 10 tissue types, which had been screened both by shRNA and CRISPR techniques. We observed poor consistency of the essentiality scores between the two screens for the majority of the cell lines. The consistency did not improve after correcting the off-target effects in the shRNA screening, suggesting a minimal impact of off-target effects. We considered a linear regression model where the shRNA essentiality score is the predictor and the CRISPR essentiality score is the response variable. We showed that by including molecular features such as mutation, gene expression and copy number variation as covariates, the predictability of the regression model greatly improved, suggesting that molecular features may provide critical information in explaining the discrepancy between the shRNA and CRISPR-based essentiality scores. We provided a Combined Essentiality Score (CES) based on the model prediction and showed that the CES greatly improved the consensus of common essential genes. Furthermore, the CES also identified novel essential genes that are specific to individual cell types. Taken together, we provided a systematic approach to define a more accurate gene essentiality profile by integrating functional screen data and molecular profiles.

Background: Intraductal papillary mucinous neoplasm (IPMNs) of the pancreas may evolve from low- to high-grade dysplasia to invasive cancer. Accurate discrimination of IPMN-associated grade of dysplasia is an unmet clinical need for appropriate patient management and treatment. This study used an integrated metabolomics and lipidomics approach, aiming to define the metabolic profiles of IPMNs. Methods: Metabolomic and lipidomic profiles of peri-operative pancreatic cyst fluid and pre-operative fasted plasma from IPMN and serous cystic neoplasm (SCN) patients were determined single-blinded in this pancreas resection cohort (n=31). Targeted (semi)quantitative analysis of 100 metabolites from 24 classes and >1000 lipid species spanning 13 classes were performed. The datasets were correlated against histological diagnosis and clinical parameters after correction for confounding factors. Group classification and model performance was done with Partial Least Squares Discriminant Analysis (PLS- DA) and a Leave-One-Out cross validation (LOO-CV) strategy, respectively. Variable Importance in Projection (VIP) ranking scores were used to select the best explanatory molecules. Findings: Over 1000 different compounds were identified in plasma and cyst fluid. The IPMN biofluid profiles showed significant lipid pathway alterations compared to SCN controls. Integrated metabolomics and lipidomics data modeling allowed accurate discrimination between IPMN and SCN and could determine the IPMN-associated grade of dysplasia. Correlations were found between plasma lipid compounds of free fatty acids, ceramides, and triacylglycerol classes and the circulating levels of CA19-9, albumin and bilirubin (r>0·6, p

We investigated whether (1) subcutaneous adipose tissue (SAT) mitochondrial capacity predicts weight loss success and (2) weight loss ameliorates obesity-related SAT mitochondrial abnormalities. SAT biopsies were obtained from 19 clinically healthy obese subjects (body mass index (BMI) 34.6±2.7 kg m–2) during a weight loss intervention (0, 5 and 12 months) and from 19 lean reference subjects (BMI 22.7±1.1 kg m–2) at baseline. Based on 1-year weight loss outcome, the subjects were divided into two groups: continuous weight losers (WL, n=6) and weight regainers (WR, n=13). Main outcome measures included SAT mitochondrial pathways from transcriptomics, mitochondrial amount (mitochondrial DNA (mtDNA), Porin protein levels), mtDNA-encoded transcripts, oxidative phosphorylation (OXPHOS) proteins, and plasma metabolites of the mitochondrial branched-chain amino-acid catabolism (BCAA) pathway. SAT and visceral adipose tissue (VAT) glucose uptake was measured with positron emission tomography. Despite similar baseline clinical characteristics, SAT in the WL group exhibited higher gene expression level of nuclear-encoded mitochondrial pathways (P=0.0224 OXPHOS, P=0.0086 tricarboxylic acid cycle, P=0.0074 fatty acid beta-oxidation and P=0.0122 BCAA), mtDNA transcript COX1 (P=0.0229) and protein level of Porin (P=0.0462) than the WR group. Many baseline mitochondrial parameters correlated with WL success, and with SAT and VAT glucose uptake. During WL, the nuclear-encoded mitochondrial pathways were downregulated, together with increased plasma metabolite levels of BCAAs in both groups. MtDNA copy number increased in the WR group at 5 months (P=0.012), but decreased to baseline level between 5 and 12 months (P=0.015). The only significant change in the WL group for mtDNA was a reduction between 5 and 12 months (P=0.004). The levels of Porin did not change in either group upon WL. Higher mitochondrial capacity in SAT predicts good long-term WL success. WL does not ameliorate SAT mitochondrial downregulation and based on pathway expression, may paradoxically further reduce it. The transcriptomics data generated in this study have been deposited to the Gene Expression Omnibus public repository, accession number GSE103769.

Background: Inflammatory bowel disease [IBD] is considered to result from the interplay between host and intestinal microbiota but its pathogenesis is incompletely understood. While IBD in adults has shown to be associated with marked changes in body fluid metabolomics, there are only few studies in children. Hence, this prospective study addressed the faecal and serum metabolomics in newly diagnosed paediatric IBD. Methods: Paediatric patients with IBD undergoing diagnostic endoscopies and controls also with endoscopy but no signs of inflammation provided blood and stool samples in a tertiary care hospital. Blood inflammatory markers and faecal calprotectin levels were determined. The serum and faecal metabolomics were determined using ultra-high pressure liquid chromatography coupled to a mass spectrometer. Results: Serum and faecal metabolite profiles in newly diagnosed paediatric IBD patients were different from healthy controls and categorized Crohn’s disease and ulcerative colitis [UC] patients into separate groups. In serum, amino acid metabolism, folate biosynthesis and signalling pathways were perturbed in Crohn’s disease; in UC also sphingolipid metabolic pathways were perturbed when compared to controls. In faecal samples, there was an increased level of several metabolites in UC in contrast to low or intermediate levels in Crohn’s disease. There was a clear correlation with the level of inflammation, i.e. faecal calprotectin levels and the profile of various biologically important metabolites [carnosine, ribose and, most significantly, choline]. Conclusion: Characterization of inflammatory pattern using metabolomics analysis is a promising tool for better understanding disease pathogenesis of paediatric IBD.

Summary Fluoropyrimidines are the first-line treatment for colorectal cancer, but their efficacy is highly variable between patients. We queried whether gut microbes, a known source of inter-individual variability, impacted drug efficacy. Combining two tractable genetic models, the bacterium E. coli and the nematode C. elegans, we performed three-way high-throughput screens that unraveled the complexity underlying host-microbe-drug interactions. We report that microbes can bolster or suppress the effects of fluoropyrimidines through metabolic drug interconversion involving bacterial vitamin B6, B9, and ribonucleotide metabolism. Also, disturbances in bacterial deoxynucleotide pools amplify 5-FU-induced autophagy and cell death in host cells, an effect regulated by the nucleoside diphosphate kinase ndk-1. Our data suggest a two-way bacterial mediation of fluoropyrimidine effects on host metabolism, which contributes to drug efficacy. These findings highlight the potential therapeutic power of manipulating intestinal microbiota to ensure host metabolic health and treat disease., Graphical Abstract, Highlights • Drug-microbe-host high-throughput screens reveal new mechanisms for cancer drugs • Microbes integrate nutritional and drug cues regulating treatment efficacy in the host • Ribonucleotide co-metabolism of cancer pro-drugs exists between host and microbe • Imbalanced bacterial deoxynucleotides synergize 5-FU-induced autophagic cell death, A three-way high-throughput screen involving host-microbe-drug interactions reveals that the beneficial impact of some drugs can be due to effects of drug-dependent alterations by gut microbe composition rather than direct action of the therapeutic itself.

Evasion of clinical interventions by Streptococcus pneumoniae occurs through selection of non-susceptible genomic variants. We report whole-genome sequencing of 3,085 pneumococcal carriage isolates from a 2.4-km 2 refugee camp. This sequencing provides unprecedented resolution of the process of recombination and its impact on population evolution. Genomic recombination hotspots show remarkable consistency between lineages, indicating common selective pressures acting at certain loci, particularly those associated with antibiotic resistance. Temporal changes in antibiotic consumption are reflected in changes in recombination trends, demonstrating rapid spread of resistance when selective pressure is high. The highest frequencies of receipt and donation of recombined DNA fragments were observed in non-encapsulated lineages, implying that this largely overlooked pneumococcal group, which is beyond the reach of current vaccines, may have a major role in genetic exchange and the adaptation of the species as a whole. These findings advance understanding of pneumococcal population dynamics and provide information for the design of future intervention strategies. © 2014 Nature America, Inc. © 2014 Nature America, Inc.

The recent growth in publicly available sequence data has introduced new opportunities for studying microbial evolution and spread. Because the pace of sequence accumulation tends to exceed the pace of experimental studies of protein function and the roles of individual amino acids, statistical tools to identify meaningful patterns in protein diversity are essential. Large sequence alignments from fast-evolving micro-organisms are particularly challenging to dissect using standard tools from phylogenetics and multivariate statistics because biologically relevant functional signals are easily masked by neutral variation and noise. To meet this need, a novel computational method is introduced that is easily executed in parallel using a cluster environment and can handle thousands of sequences with minimal subjective input from the user. The usefulness of this kind of machine learning is demonstrated by applying it to nearly 5000 haemagglutinin sequences of influenza A/H3N2.Antigenic and 3D structural mapping of the results show that the method can recover the major jumps in antigenic phenotype that occurred between 1968 and 2013 and identify specific amino acids associated with these changes. The method is expected to provide a useful tool to uncover patterns of protein evolution.

Mitochondrial dysfunction elicits various stress responses in different model systems, but how these responses relate to each other and contribute to mitochondrial disease has remained unclear. Mitochondrial myopathy (MM) is the most common manifestation of adult-onset mitochondrial disease and shows a multifaceted tissue-specific stress response: (1) transcriptional response, including metabolic cytokines FGF21 and GDF15; (2) remodeling of one-carbon metabolism; and (3) mitochondrial unfolded protein response. We show that these processes are part of one integrated mitochondrial stress response (ISRmt), which is controlled by mTORC1 in muscle. mTORC1 inhibition by rapamycin downregulated all components of ISRmt, improved all MM hallmarks, and reversed the progression of even late-stage MM, without inducing mitochondrial biogenesis. Our evidence suggests that (1) chronic upregulation of anabolic pathways contributes to MM progression, (2) long-term induction of ISRmt is not protective for muscle, and (3) rapamycin treatment trials should be considered for adult-type MM with raised FGF21.