Pan-cancer proteogenomics characterization of tumor immunity.

Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%-20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype-specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents.

Enrichr-KG: bridging enrichment analysis across multiple libraries.

Gene and protein set enrichment analysis is a critical step in the analysis of data collected from omics experiments. Enrichr is a popular gene set enrichment analysis web-server search engine that contains hundreds of thousands of annotated gene sets. While Enrichr has been useful in providing enrichment analysis with many gene set libraries from different categories, integrating enrichment results across libraries and domains of knowledge can further hypothesis generation. To this end, Enrichr-KG is a knowledge graph database and a web-server application that combines selected gene set libraries from Enrichr for integrative enrichment analysis and visualization. The enrichment results are presented as subgraphs made of nodes and links that connect genes to their enriched terms. In addition, users of Enrichr-KG can add gene-gene links, as well as predicted genes to the subgraphs. This graphical representation of cross-library results with enriched and predicted genes can illuminate hidden associations between genes and annotated enriched terms from across datasets and resources. Enrichr-KG currently serves 26 gene set libraries from different categories that include transcription, pathways, ontologies, diseases/drugs, and cell types. To demonstrate the utility of Enrichr-KG we provide several case studies. Enrichr-KG is freely available at: https://maayanlab.cloud/enrichr-kg.

GeneRanger and TargetRanger: processed gene and protein expression levels across cells and tissues for target discovery.

Several atlasing efforts aim to profile human gene and protein expression across tissues, cell types and cell lines in normal physiology, development and disease. One utility of these resources is to examine the expression of a single gene across all cell types, tissues and cell lines in each atlas. However, there is currently no centralized place that integrates data from several atlases to provide this type of data in a uniform format for visualization, analysis and download, and via an application programming interface. To address this need, GeneRanger is a web server that provides access to processed data about gene and protein expression across normal human cell types, tissues and cell lines from several atlases. At the same time, TargetRanger is a related web server that takes as input RNA-seq data from profiled human cells and tissues, and then compares the uploaded input data to expression levels across the atlases to identify genes that are highly expressed in the input and lowly expressed across normal human cell types and tissues. Identified targets can be filtered by transmembrane or secreted proteins. The results from GeneRanger and TargetRanger are visualized as box and scatter plots, and as interactive tables. GeneRanger and TargetRanger are available from https://generanger.maayanlab.cloud and https://targetranger.maayanlab.cloud, respectively.

The unexpected influence of legacy conspecific density dependence.

When plants die, neighbours escape competition. Living conspecifics could disproportionately benefit because they are freed from negative intraspecific processes; however, if the negative effects of past conspecific neighbours persist, other species might be advantaged, and diversity might be maintained through legacy effects. We examined legacy effects in a mapped forest by modelling the survival of 37,212 trees of 23 species using four neighbourhood properties: living conspecific, living heterospecific, legacy conspecific (dead conspecifics) and legacy heterospecific densities. Legacy conspecific effects proved nearly four times stronger than living conspecific effects; changes in annual survival associated with legacy conspecific density were 1.5% greater than living conspecific effects. Over 90% of species were negatively impacted by legacy conspecific density, compared to 47% by living conspecific density. Our results emphasize that legacies of trees alter community dynamics, revealing that prior research may have underestimated the strength of density dependent interactions by not considering legacy effects.

SigCom LINCS: data and metadata search engine for a million gene expression signatures.

Millions of transcriptome samples were generated by the Library of Integrated Network-based Cellular Signatures (LINCS) program. When these data are processed into searchable signatures along with signatures extracted from Genotype-Tissue Expression (GTEx) and Gene Expression Omnibus (GEO), connections between drugs, genes, pathways and diseases can be illuminated. SigCom LINCS is a webserver that serves over a million gene expression signatures processed, analyzed, and visualized from LINCS, GTEx, and GEO. SigCom LINCS is built with Signature Commons, a cloud-agnostic skeleton Data Commons with a focus on serving searchable signatures. SigCom LINCS provides a rapid signature similarity search for mimickers and reversers given sets of up and down genes, a gene set, a single gene, or any search term. Additionally, users of SigCom LINCS can perform a metadata search to find and analyze subsets of signatures and find information about genes and drugs. SigCom LINCS is findable, accessible, interoperable, and reusable (FAIR) with metadata linked to standard ontologies and vocabularies. In addition, all the data and signatures within SigCom LINCS are available via a well-documented API. In summary, SigCom LINCS, available at https://maayanlab.cloud/sigcom-lincs, is a rich webserver resource for accelerating drug and target discovery in systems pharmacology.

Water, mineral, and blood acid-base balance in calves with naturally occurring diarrhea receiving two alternative oral rehydration solutions or a placebo.

Quantifying the water and mineral losses in feces is essential to determine the optimal composition of oral rehydration solutions (ORS) for diarrheic animals. In a randomized complete block design, this study evaluated water, mineral, and blood acid-base balance of calves with naturally occurring diarrhea receiving ORS or a placebo. On d 0, 45 calves (age: 18 ± 3.2 d; mean ± SD) were selected based on the presence of visual signs of diarrhea, such as dirty tail or wet feces, along with clinical symptoms evaluated by measuring the skin turgor and the degree of enophthalmos. On d 1, calves were divided into blocks of 3 animals based on blood base excess (BE) measured at 0900 h, and within each block, calves were randomly assigned to 1 of 3 treatments (15 calves per treatment) including (1) a hypertonic ORS (HYPER; Na+ = 110 mmol/L; 370 mOsm/kg; strong ion difference [SID] = 60 mEq/L), (2) a hypotonic ORS with low Na+ (HYPO; Na+ = 77 mmol/L; 278 mOsm/kg; SID = 71 mEq/L), and (3) a placebo consisting of lukewarm water with 5 g/L of whey powder (CON). Milk replacer (MR) was fed through teat buckets twice daily at 0630 h and 1700 h in 2 equally sized meals of 2.5 L from d 1 to 3 and of 3.0 L on d 4 and 5. Treatments consisting of 2.0 L lukewarm solutions were administered between milk meals from d 1 to 3 at 1200 h and 2030 h through teat buckets. Refusals of MR and treatments were recorded daily, and blood samples were collected from the jugular vein once daily at arrival in the afternoon of d 0 and at 0900 h from d 1 to 5 after arrival. Urine and feces were collected quantitatively over a 48-h period from 1200 h on d 1 to 1200 h on d 3, and a representative sample of each 24-h period was stored. In addition, the volume of extracellular fluid was evaluated on d 2 by postprandial sampling over a 4-h period relative to the injection of sodium thiosulfate at 1300 h. Total daily fluid intake (MR, treatment, and water) from d 1 to 3 was greater in HYPER (LSM ± SEM; 8.9 ± 0.36 L/d) and HYPO (7.8 ± 0.34 L/d) than in CON (6.6 ± 0.34 L/d). This resulted in a greater water balance (water intake - fluid output in urine and feces) in calves receiving ORS (59.6 ± 6.28 g/kg BW per 24 h vs. 39.6 ± 6.08 g/kg BW per 24 h). Fecal Na+ losses were greater in HYPER than in the other treatments (81 ± 12.0 mg/kg BW per 24 h vs. 24 ± 11.8 mg/kg BW per 24 h). Blood pH was higher in HYPO (7.41 ± 0.016) than CON (7.35 ± 0.016) over the 5 monitoring days, whereas HYPER (7.37 ± 0.017) did not differ with other treatments. In this experimental model, diarrheic calves were likely unable to absorb the high Na+ load from HYPER, resulting in greater Na+ losses in feces, which might have impaired the alkalinizing capacity of HYPER. In contrast, HYPO significantly sustained blood acid-base balance compared with CON, whereas HYPER did not. This suggests that low tonicity ORS with a high SID are more suitable for diarrheic calves.

Longitudinal characterization of the metabolome of dairy cows transitioning from one lactation to the next: Investigations in blood serum.

The objective of this study was to characterize changes in the serum metabolome and various indicators of oxidative balance in dairy cows starting 2 wk before dry-off and continuing until wk 16 of lactation. Twelve Holstein dairy cows (body weight 745 ± 71 kg, body condition score 3.43 ± 0.66; mean ± SD) were housed in a tiestall barn from 10 wk before to 16 wk after parturition. Cows were dried off 6 wk before the expected calving date (mean dry period length = 42 d). From 8 wk before calving to 16 wk after calving, blood samples were taken weekly to study redox metabolism by determining antioxidant capacity, measured as the ferric-reducing ability of plasma, reactive oxidative metabolites, oxidative stress index, oxidative damage of lipids, measured as thiobarbituric acid reactive substances, and glutathione peroxidase activity. According to these results, dairy cows had the lowest serum antioxidant capacity and greater levels of oxidative stress during the dry-off period and the early postpartum period. For metabolomics, a subset of serum samples including wk -7 (before dry-off), -5 (after dry-off), -1, 1, 5, 10, and 15 relative to calving were used. A targeted metabolomics approach was performed using liquid chromatography and flow injection with electrospray ionization triple quadrupole mass spectrometry using the MxP Quant 500 kit (Biocrates Life Sciences AG). A total of 240 metabolites in serum were used in the final data analysis. Principal component analysis revealed a clear separation by days of sampling, indicating a remarkable shift in metabolic phenotype between the dry period and late and early lactation. Changes in many non-lipid metabolites associated with one-carbon metabolism, the tricarboxylic acid cycle, the urea cycle, and AA catabolism were observed in the study, with changes in AA serum concentrations likely related to factors such as energy and nitrogen balance, digestive efficiency, and changing diets. The study confirmed an extensive remodeling of the serum lipidome in peripartum dairy cows, highlighting the importance of changes in acylcarnitine (acylCN), phosphatidylcholines (PC), and triacylglycerols (TG), as they play a crucial role in lipid metabolism. Results showed that short-chain acylCN increased after dry-off and decreased thereafter, whereas lipid-derived acylCN increased around parturition, suggesting that more fatty acids could enter mitochondria. Phospholipids and sphingolipids in serum showed changes during lactation. In particular, concentrations of sphingomyelins, PC, and lysoPC decreased around calving but increased in mid- and late lactation. In contrast, concentrations of TG remained consistently low after parturition. The serum concentrations of bile acids fluctuated during the dry period and lactation, with glycocholic acid, cholic acid, glycodeoxycholic acid, and taurocholic acid showing the greatest concentrations. These changes are likely due to the interplay of diet, liver function, and the ability of the gut microbiota to convert primary to secondary bile acids. Overall, these descriptive results may aid in hypothesis generation and in the design and interpretation of future metabolite-based studies in dairy cows. Furthermore, they contribute to our understanding of the physiological ranges in serum metabolites relative to the lactation cycle of the dairy cow.

Longitudinal characterization of the metabolome of dairy cows transitioning from one lactation to the next: Investigations in the liver.

This study aimed to investigate the metabolic changes in the livers of dairy cows from 1 wk before dry off to 1 wk after calving. Twelve high-yielding Holstein cows were included in a longitudinal study and housed in a tiestall barn. The cows were dried off at 6 wk before the expected calving date (dry period length = 42 d). During the entire lactation, the cows were milked twice daily at 0600 and 1700 h. Liver biopsies were taken from each cow at 4 different times: wk -7 (before drying off), -5 (after drying off), -1 and +1 relative to calving. A targeted metabolomics approach was performed by liquid chromatography and flow injection with electrospray ionization triple quadrupole mass spectrometry using the MxP Quant 500 kit (Biocrates Life Sciences AG). A total of 185 metabolites in the liver were used for the final data analysis. Principal component analysis revealed a clear separation by days of sampling, indicating a notable shift in metabolic phenotype from late lactation to the dry period and further changes after calving. Changes were observed in several classes of compounds, including AA and biogenic amines. In particular, the changes in acylcarnitines (AcylCN), phosphatidylcholines (PC), sphingomyelins (SM), and bile acids (BA) indicated extensive remodeling of the hepatic lipidome. The changes in AcylCN concentrations in early lactation suggest incomplete fatty acid oxidation in the liver, possibly indicating mitochondrial dysfunction or enzymatic imbalance. In addition, the changes in PC and SM species in early lactation indicate altered cell membrane composition, which may affect cell signaling and functionality. In addition, changes in BA concentrations and profiles indicate dynamic adaptations in BA synthesis, as well as lipid digestion and absorption during the observation period. In particular, principal component analysis showed an overlapping distribution of liver metabolites in primiparous and multiparous cows, indicating no significant difference between these groups. In addition, Volcano plots showed similar liver metabolism between primiparous and multiparous cows, with no significant fold changes (>1.5) in any metabolite at significant P-values (false discovery rate <0.05). These results provide valuable insight into the physiological ranges of liver metabolites during dry period and calving in healthy dairy cows and should contribute to the design and interpretation of future metabolite-based studies of the transition dairy cow.

A qualitative study to explore the symptoms and impacts of Crohn's disease and to develop the Crohn's Disease Diary.

PURPOSE: To explore symptoms and disease impacts of Crohn's disease and to develop a new patient-reported outcomes (PRO) measure according to industry best practices. METHODS: A conceptual model of relevant symptoms experienced by patients with Crohn's disease was developed following a literature review. Three rounds of combined qualitative semi-structured concept elicitation and cognitive debriefing interviews with 36 patients (≥ 16 years) with Crohn's disease and 4 clinicians were conducted to further explore the most commonly reported and most bothersome symptoms to patients. Interview results were used to update the conceptual model as well as items and response options included in The Crohn's Disease Diary, a new PRO measure. RESULTS: All patients (N = 36) reported abdominal pain, loose or liquid bowel movements, and high or increased frequency of bowel movements, with most reporting these symptoms spontaneously (100%, 92%, and 75%, respectively). All patients reported bowel movement urgency, but 61% reported this symptom only when probed. Most also reported that symptoms impacted activities of daily living, work/school, and emotional, social, and physical functioning (overall, 78%-100%; spontaneously, 79% - 92%). Data regarding core symptoms of Crohn's disease from clinician concept elicitation interviews supported patient data. The 17-item Crohn's Disease Diary assesses core symptoms and impacts of Crohn's disease over 24 h, and extraintestinal manifestations over 7 days. The content validity of the diary was confirmed during cognitive debriefing interviews. CONCLUSION: The Crohn's Disease Diary is a new PRO measure for the assessment of Crohn's disease symptoms and impacts, developed according to industry best practices.

Transforming L1000 profiles to RNA-seq-like profiles with deep learning.

The L1000 technology, a cost-effective high-throughput transcriptomics technology, has been applied to profile a collection of human cell lines for their gene expression response to > 30,000 chemical and genetic perturbations. In total, there are currently over 3 million available L1000 profiles. Such a dataset is invaluable for the discovery of drug and target candidates and for inferring mechanisms of action for small molecules. The L1000 assay only measures the mRNA expression of 978 landmark genes while 11,350 additional genes are computationally reliably inferred. The lack of full genome coverage limits knowledge discovery for half of the human protein coding genes, and the potential for integration with other transcriptomics profiling data. Here we present a Deep Learning two-step model that transforms L1000 profiles to RNA-seq-like profiles. The input to the model are the measured 978 landmark genes while the output is a vector of 23,614 RNA-seq-like gene expression profiles. The model first transforms the landmark genes into RNA-seq-like 978 gene profiles using a modified CycleGAN model applied to unpaired data. The transformed 978 RNA-seq-like landmark genes are then extrapolated into the full genome space with a fully connected neural network model. The two-step model achieves 0.914 Pearson's correlation coefficients and 1.167 root mean square errors when tested on a published paired L1000/RNA-seq dataset produced by the LINCS and GTEx programs. The processed RNA-seq-like profiles are made available for download, signature search, and gene centric reverse search with unique case studies.

DrugShot: querying biomedical search terms to retrieve prioritized lists of small molecules.

BACKGROUND: PubMed contains millions of abstracts that co-mention terms that describe drugs with other biomedical terms such as genes or diseases. Unique opportunities exist for leveraging these co-mentions by integrating them with other drug-drug similarity resources such as the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 signatures to develop novel hypotheses. RESULTS: DrugShot is a web-based server application and an Appyter that enables users to enter any biomedical search term into a simple input form to receive ranked lists of drugs and other small molecules based on their relevance to the search term. To produce ranked lists of small molecules, DrugShot cross-references returned PubMed identifiers (PMIDs) with DrugRIF or AutoRIF, which are curated resources of drug-PMID associations, to produce an associated small molecule list where each small molecule is ranked according to total co-mentions with the search term from shared PubMed IDs. Additionally, using two types of drug-drug similarity matrices, lists of small molecules are predicted to be associated with the search term. Such predictions are based on literature co-mentions and signature similarity from LINCS L1000 drug-induced gene expression profiles. CONCLUSIONS: DrugShot prioritizes drugs and small molecules associated with biomedical search terms. In addition to listing known associations, DrugShot predicts additional drugs and small molecules related to any search term. Hence, DrugShot can be used to prioritize drugs and preclinical compounds for drug repurposing and suggest indications and adverse events for preclinical compounds. DrugShot is freely and openly available at: https://maayanlab.cloud/drugshot and https://appyters.maayanlab.cloud/#/DrugShot .

The functional form of specialised predation affects whether Janzen-Connell effects can prevent competitive exclusion.

Janzen-Connell effects (JCEs), specialised predation of seeds and seedlings near conspecific trees, are hypothesised to maintain species richness. While previous studies show JCEs can maintain high richness relative to neutral communities, recent theoretical work indicates JCEs may weakly inhibit competitive exclusion when species exhibit interspecific fitness variation. However, recent models make somewhat restrictive assumptions about the functional form of specialised predation-that JCEs occur at a fixed rate when offspring are within a fixed distance of a conspecific tree. Using a theoretical model, I show that the functional form of JCEs largely impacts their ability to maintain coexistence. If predation pressure increases additively with adult tree density and decays exponentially with distance, JCEs maintain considerably higher species richness than predicted by recent models. Loosely parameterising the model with data from a Panamanian tree community, I elucidate the conditions under which JCEs are capable of maintaining high species richness.

LINCS Data Portal 2.0: next generation access point for perturbation-response signatures.

The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program with the goal of generating a large-scale and comprehensive catalogue of perturbation-response signatures by utilizing a diverse collection of perturbations across many model systems and assay types. The LINCS Data Portal (LDP) has been the primary access point for the compendium of LINCS data and has been widely utilized. Here, we report the first major update of LDP (http://lincsportal.ccs.miami.edu/signatures) with substantial changes in the data architecture and APIs, a completely redesigned user interface, and enhanced curated metadata annotations to support more advanced, intuitive and deeper querying, exploration and analysis capabilities. The cornerstone of this update has been the decision to reprocess all high-level LINCS datasets and make them accessible at the data point level enabling users to directly access and download any subset of signatures across the entire library independent from the originating source, project or assay. Access to the individual signatures also enables the newly implemented signature search functionality, which utilizes the iLINCS platform to identify conditions that mimic or reverse gene set queries. A newly designed query interface enables global metadata search with autosuggest across all annotations associated with perturbations, model systems, and signatures.

EnrichrBot: Twitter bot tracking tweets about human genes.

MOTIVATION: Micro-blogging with Twitter to communicate new results, discuss ideas and share techniques is becoming central. While most Twitter users are real people, the Twitter API provides the opportunity to develop Twitter bots and to analyze global trends in tweets. RESULTS: EnrichrBot is a bot that tracks and tweets information about human genes implementing six principal functions: (i) tweeting information about under-studied genes including non-coding lncRNAs, (ii) replying to requests for information about genes, (iii) responding to GWASbot, another bot that tweets Manhattan plots from genome-wide association study analysis of the UK Biobank, (iv) tweeting randomly selected gene sets from the Enrichr database for analysis with Enrichr, (v) responding to mentions of human genes in tweets with additional information about these genes and (vi) tweeting a weekly report about the most trending genes on Twitter. AVAILABILITY AND IMPLEMENTATION: https://twitter.com/botenrichr; source code: https://github.com/MaayanLab/EnrichrBot. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Effects of supplemental calcium gluconate embedded in a hydrogenated fat matrix on lactation, digestive, and metabolic variables in dairy cattle.

There is growing evidence suggesting that by improving gut integrity and function, less energy is partitioned toward immune responses related to xenobiotic infiltration, sparing energy for productive purposes. Gluconic acid and its salts have previously shown prebiotic effects in the lower gut of nonruminant animals, where they serve as a precursor for butyrate, although evidence in ruminants is limited. Butyrate and its fermentative precursors have demonstrated multiple beneficial effects to gastrointestinal ecology, morphology, and function, such as the stimulation of epithelial cell proliferation and improvement of gut barrier function and ecology. The objective of this study was to evaluate changes in milk production, milk fatty acid composition, and fecal and blood parameters in lactating dairy cattle fed a hydrogenated fat-embedded calcium gluconate (HFCG) supplement designed to target the hindgut for calcium gluconate delivery. In addition, the effects of a compound feed processing method (i.e., incorporated into a mash or an extruded pellet) were tested to evaluate the effect of extrusion on product efficacy. Forty-five lactating Holstein cows at approximately 165 d in milk were used in a 3 × 3 Latin square consisting of three 28-d periods, during which animals were offered a basal ration mixed with 3 different compound feeds: a negative control in mash form containing no HFCG, or the HFCG supplement fed at a target rate of 16 g/d, delivered in either a mash or pelleted form. Supplementation of HFCG tended to increase yields of milk fat and fat- and energy-corrected milk. Total yields and concentrations of milk fatty acids ≥18 carbons in length tended to increase in response to HFCG. Plasma nonesterified fatty acids and milk urea increased in HFCG treatments. No differences were observed in fecal pH or fecal concentrations of volatile fatty acids, with the exception of isobutyrate, which decreased in HFCG-fed cows. Changes in milk fatty acid profile suggest that increased milk fat yield was driven by increased incorporation of preformed fatty acids, supported by increased circulating nonesterified fatty acid. Future research investigating the mode of action of HFCG at the level of the hindgut epithelium is warranted, as measured fecal parameters showed no response to treatment.

eXpression2Kinases (X2K) Web: linking expression signatures to upstream cell signaling networks.

While gene expression data at the mRNA level can be globally and accurately measured, profiling the activity of cell signaling pathways is currently much more difficult. eXpression2Kinases (X2K) computationally predicts involvement of upstream cell signaling pathways, given a signature of differentially expressed genes. X2K first computes enrichment for transcription factors likely to regulate the expression of the differentially expressed genes. The next step of X2K connects these enriched transcription factors through known protein-protein interactions (PPIs) to construct a subnetwork. The final step performs kinase enrichment analysis on the members of the subnetwork. X2K Web is a new implementation of the original eXpression2Kinases algorithm with important enhancements. X2K Web includes many new transcription factor and kinase libraries, and PPI networks. For demonstration, thousands of gene expression signatures induced by kinase inhibitors, applied to six breast cancer cell lines, are provided for fetching directly into X2K Web. The results are displayed as interactive downloadable vector graphic network images and bar graphs. Benchmarking various settings via random permutations enabled the identification of an optimal set of parameters to be used as the default settings in X2K Web. X2K Web is freely available from http://X2K.cloud.

Evaluation of predicted ration nutritional values by NRC (2001) and INRA (2018) feed evaluation systems, and implications for the prediction of milk response.

Net energy and protein systems (hereafter called feed evaluation systems) offer the possibility to formulate rations by matching feed values (e.g., net energy and metabolizable protein) with animal requirements. The accuracy and precision of this approach relies heavily on the quantification of various animal digestive and metabolic responses to dietary changes. Therefore, the aims of the current study were, first, to evaluate the predicted responses to dietary changes of total-tract digestibility (including organic matter, crude protein, and neutral detergent fiber) and nitrogen (N) flows at the duodenum (including microbial N and undigested feed N together with endogenous N) against measurements from published studies by 2 different feed evaluation systems. These feed evaluation systems were the recently updated Institut National de la Recherche Agronomique (INRA, 2018) and the older, yet widely used, National Research Council (NRC, 2001) system. The second objective was to estimate the accuracy and precision of predicting milk yield responses based on values of net energy (NEL) and metabolizable protein (MP) supply predicted by the 2 feed evaluation systems. For this, published studies, with experimentally induced changes in either NEL or MP content, were used to calibrate the relationship of NEL and MP supply, with milk component yields. Based on the slope, root mean square prediction error, and concordance correlation coefficient (CCC), the results obtained show that total nonammonia nitrogen flow at the duodenum was predicted with similar accuracy and precision, but considerably better prediction was achieved when the INRA model was used to predict organic matter and neutral detergent fiber digestibility responses. The average NEL and MP content predicted by both models was similar, but NEL and MP content of individual diets differed substantially between both models as indicated by determination coefficients of 0.45 (NEL content) and 0.50 (MP content). Despite these differences, this work shows that when response equations are calibrated with NEL and MP values either from the INRA model or from the NRC model, the accuracy and precision (slope, root mean square prediction error, and CCC) of the predicted milk component yields responses is similar between the models. The lowest accuracy and precision were observed for milk fat yield response, with CCC values in the range of 0.37 to 0.40, compared with milk lactose and protein yields responses for which CCC values were in the range of 0.75 to 0.81.

Total-tract digestibility and milk productivity of dairy cows as affected by trace mineral sources.

The chemical characteristics associated with different sources of Cu, Zn, and Mn such as sulfate, hydroxychloride, or organic chelate may affect the interaction between the metals and other components present within the gut of a ruminant (i.e., microorganisms and nutrients). The present study aimed to evaluate the effect of different supplemental trace mineral strategies on apparent total-tract digestibility, rumen fermentation, and dairy productivity. Using 52 Holstein cows in a replicated 4 × 4 Latin square design with periods of 21 d, 4 treatments differing in their sources of Cu, Zn, and Mn were tested: sulfate form, hydroxychloride form, a mix of sulfate and organic chelate forms (70 and 30%, respectively), and a mix of hydroxychloride and organic chelate forms (70 and 30%, respectively). Treatments were formulated to provide 15, 40, and 20 mg of supplemental Cu, Zn, and Mn, respectively, per kilogram of dry matter. This level of supplementation, together with the basal level present in forages and feed ingredients, resulted in a total average supply of 19, 79, and 84 mg of Cu, Zn, and Mn, respectively, per kilogram of dry matter. Cows had ad libitum access to a total mixed ration, which provided 15.3% of crude protein, 21.7% of starch, and 35.3% of neutral detergent fiber (NDF). Data were summarized by period with trace minerals and period as fixed effects and the repeated cow as random effect using the MIXED procedure of SAS (SAS Institute Inc., Cary, NC). Apparent total-tract NDF and crude protein digestibility was reduced (-0.8% and -1.0%, respectively) when organic chelate trace minerals were fed, whereas apparent total-tract NDF digestibility was improved (+0.8%) when sulfate trace minerals were replaced by hydroxychloride trace minerals. Cows supplemented with the hydroxychloride source had lower ruminal butyric acid concentration compared with cows fed sulfate trace minerals (13.3 vs. 14.6%). In addition, fat- and protein-corrected milk and milk fat yields were improved (+1.0 kg/d and +51 g/d, respectively) in multiparous cows when trace minerals were supplemented as hydroxychloride compared with sulfate. These effects were not observed in primiparous cows. These results confirm that trace mineral sources affect apparent total-tract digestibility and indicate that milk productivity may also be affected.

Outcomes from massive paracetamol overdose: a retrospective observational study.

LINKED ARTICLE: This article is commented on by Bateman DN and Dear JW. Should we treat very large paracetamol overdose differently? Br J Clin Pharmacol 2017; 83: 1163-5. https://doi.org/10.1111/bcp.13279 AIMS: Treatment of paracetamol (acetaminophen) overdose with acetylcysteine is standardized, with dose determined only by patient weight. The validity of this approach for massive overdoses has been questioned. We systematically compared outcomes in massive and non-massive overdoses, to guide whether alternative treatment strategies should be considered, and whether the ratio between measured timed paracetamol concentrations (APAPpl ) and treatment nomogram thresholds at those time points (APAPt ) provides a useful assessment tool. METHODS: This is a retrospective observational study of all patients (n = 545) between 2005 and 2013 admitted to a tertiary care toxicology service with acute non-staggered paracetamol overdose. Massive overdoses were defined as extrapolated 4-h plasma paracetamol concentrations >250 mg l-1 , or reported ingestions ≥30 g. Outcomes (liver injury, coagulopathy and kidney injury) were assessed in relation to reported dose and APAPpl :APAPt ratio (based on a treatment line through 100 mg l-1 at 4 h), and time to acetylcysteine. RESULTS: Ingestions of ≥30 g paracetamol correlated with higher peak serum aminotransferase (r = 0.212, P < 0.0001) and creatinine (r = 0.138, P = 0.002) concentrations. Acute liver injury, hepatotoxicity and coagulopathy were more frequent with APAPpl :APAPt  ≥ 3 with odds ratios (OR) and 95% confidence intervals (CI) of 9.19 (5.04-16.68), 35.95 (8.80-158.1) and 8.34 (4.43-15.84), respectively (P < 0.0001). Heightened risk persisted in patients receiving acetylcysteine within 8 h of overdose. CONCLUSION: Patients presenting following massive paracetamol overdose are at higher risk of organ injury, even when acetylcysteine is administered early. Enhanced therapeutic strategies should be considered in those who have an APAPpl :APAPt  ≥ 3. Novel biomarkers of incipient liver injury and abbreviated acetylcysteine regimens require validation in this patient cohort.

A method to estimate cow potential and subsequent responses to energy and protein supply according to stage of lactation.

Milk responses to dietary change are influenced by the relative production level, that is, the distance between observed production and potential production. The closer the animal is to its potential, the smaller the expected response is to extra nutrients. Therefore, the aim of this work was to provide a method to quantify cow potential, to estimate subsequent responses to changes in nutrient supply. The observed efficiencies in net energy for lactation (NEL) and metabolizable protein (MP) are proposed as a basis to estimate the relative production level of the animal. The rationale for using NEL and MP efficiency (ratios of milk energy yield/NEL above maintenance supply and milk protein yield/MP above maintenance supply) builds on the uniformity of the observed relationships between size of the milk responses and extra NEL supply and MP supply, when centered on a given efficiency. From there, a pivot nutritional situation where MP and NEL efficiency are 0.67 and 1.00, respectively, was defined, from which milk responses could be derived across animals varying in production potential. An implicit assumption of using response equations centered on reference efficiency pivots is that the size of the response to a fixed change in nutrient supply, relative to the pivot, is identical for animals with different production capacities. The proposed approach was evaluated with 2 independent data sets, where different dietary treatments were applied during the whole lactation. In these data sets, MP and NEL above maintenance supply were calculated weekly using the recently updated INRA Systali feed units system. Differences in NEL and MP supply above maintenance between the extreme dietary treatments were large, on average 667 g of MP/d and 13 MJ of NEL/d (3.11 Mcal/d) in the first data set, and 513 g of MP/d and 29 MJ of NEL/d (6.93 Mcal/d) for the second data set. Milk energy yield and milk component yields were predicted with root mean square prediction errors between 7.6 and 13.5% and concordance correlation coefficients between 0.784 and 0.934, respectively. Assessed by the Akaike's information criterion, significant differences existed in the accuracy of prediction for milk energy yield and milk component yields between stages of lactation. However, the effects of stage of lactation were not consistent between data sets and, for most of the predicted variables, relatively small. We concluded that the pivot concept can be used to predict milk energy yield and milk component yields responses to dietary change with a good accuracy for diets that are substantially different and across all stages of lactation.