Freelance Data Scientist I Biomarker Discovery and Statistical Bioinformatics | Expert in R

Animesh Acharjee, Dipak Kotecha, George Gkoutos, Andreas Karwath (2022). Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare .

Animesh Acharjee, Joseph Larkman, Victor Roth Cardoso, Georgios V. Gkoutos(2020). Effect of biomarker identification on power analysis for diagnostics research . Research Square

Animesh Acharjee, Joseph Larkman, Victor Roth Cardoso, Georgios V. Gkoutos(2020). PowerTools: A web based user-friendly tool for future translational study design . Research Square

P005 Regulators of the guanylate cyclase pathway are potential novel markers of mucosal healing in IBD @article{8fb07d01e558493d8a589a52093ae517, title = "P005 Regulators of the guanylate cyclase pathway are potential novel markers of mucosal healing in IBD", abstract = "Background: Intestinal guanylate cyclase C (GC-C) present in epithelial cells has been shown to have a role in gut homeostasis. The downstream effects of activation of GC-C are due to production of cyclic-GMP. GC-C is encoded by the gene GUCY2C, mutations in which are implicated in Familial Diarrhoea Syndrome and noted as risk factors for Crohn{\textquoteright}s disease. GUCY2C and its activator, GUCA2A have been shown to be downregulated in IBD. We hypothesised that regulation of this pathway might be important in remission and response to therapy.Methods: Forty-four patients with IBD and 7 patients with polyps (controls) at University Hospitals Birmingham, UK were recruited under ethical consent. Relevant demographic and clinical data were extracted from the hospital EMR. All patients had disease activity recorded on endoscopic examination of mucosa and intestinal biopsies collected for analysis. Mucosal healing was defined as MES = 0 (UC) and SES-CD <6 (CD). Of 44 patients, 14 had matched baseline and 12-week post-biologic therapy assessment and had tissues collected. Intestinal biopsies were analysed by 3{\textquoteright}RNA-sequencing using the Illumina Nextseq sequencer. FASTQ files were generated through BaseSpace and reads de-multiplexed, trimmed, aligned, and quantified using the GeneGlobe (Qiagen) workflow. Expression was compared between groups using either Wilcoxon tests or Kruskal–Wallis with Dunn post-hoc analysis as appropriate.Results: Expression of Guanylate cyclase activators GUCA2A and GUCA2B in patients who showed mucosal healing was equivalent to controls, but GUCA2A was down-regulated in those with active disease (non-healing) (p = 0.006). The same pattern was observed for transcriptional regulators of GUCY2C, including HNF4A (p = 0.0248) and CDX2 (0.0062). Correspondingly, GUCY2C was reduced in non-healing mucosa, although the difference was not significant (Figure 1). In patients who responded to biologic therapy, both GUCA2A (p = 0.0234) and GUCA2B (p = 0.0117) were increased at follow-up but no change was observed for those who did not respond. Change in GUCY2C expression did not reach statistical significance in either group, although an increase was observed for a large proportion of responders.Conclusion: Our findings suggest that regulation of the Guanylate Cyclase pathway may be involved in the restoration of a stable mucosa in IBD and that expression of its regulators may be used to indicate response to treatment.", author = "Jeffery, {L E} and Shivaji, {U N} and D Zardo and A Acharjee and Nardone, {O M} and Smith, {S C} and G Gkoutos and Visweswariah, {S S} and S Ghosh and M Iacucci", year = "2020", month = jan, day = "15", doi = "10.1093/ecco-jcc/jjz203.134", language = "English", volume = "14", pages = "S131–S132", journal = "Journal of Crohn's & Colitis", issn = "1873-9946", publisher = "Oxford University Press", number = "Supplement_1", } . Journal of Crohn's & Colitis.

Sedlackova L, Otten EG, Scialo F, Shapira D, Kataura T, Carroll B, Seranova E, Rabanal-Ruiz Y, Kelly G, Stefanatos R, et al.(2020). Autophagy promotes cell and organismal survival by maintaining NAD(H) pools .

Pal N, Acharjee A, Ament Z, Dent T, Yavari A, Mahmod M, Ariga R, West J, Steeples V, Cassar M, et al.(2019). Metabolic Profiling of Aortic Stenosis and Hypertrophic Cardiomyopathy Identifies Mechanistic Contrasts in Substrate Utilisation .

Liu K, Acharjee A, Hinz C, Liggi S, Murgia A, Denes J, Gulston MK, Wang X, Chu Y, West JA, et al.(2019). The consequences of lipid remodelling of adipocyte cell membranes being functionally distinct from global lipid storage during obesity .

Animesh Acharjee, Argyris Zardilis, Jo&#227;o Dias, James Smith(2014). Extensible and Executable Stochastic Models of Fatty Acid and Lipid Metabolism . Computational Methods in Systems Biology. p. 244--247. Springer International Publishing

Animesh Acharjee, Argyris Zardilis, Jo&#227;o Dias, James Smith(2014). Extensible and Executable Stochastic Models of Fatty Acid and Lipid Metabolism . Computational Methods in Systems Biology. p. 244--247. Springer International Publishing

Investigating the potential of a prematurely aged immune phenotype in severely injured patients as predictor of risk of sepsis @article{22033420443e41328fce33ab5d6ed9f5, title = "Investigating the potential of a prematurely aged immune phenotype in severely injured patients as predictor of risk of sepsis", abstract = "BackgroundTraumatic injury elicits a hyperinflammatory response and remodelling of the immune system leading to immuneparesis. This study aimed to evaluate whether traumatic injury results in a state of prematurely aged immune phenotype to relate this to clinical outcomes and a greater risk of developing additional morbidities post-injury.Methods and findingsBlood samples were collected from 57 critically injured patients with a mean Injury Severity Score (ISS) of 26 (range 15–75 years), mean age of 39.67 years (range 20–84 years), and 80.7% males, at days 3, 14, 28 and 60 post-hospital admission. 55 healthy controls (HC), mean age 40.57 years (range 20–85 years), 89.7% males were also recruited. The phenotype and frequency of adaptive immune cells were used to calculate the IMM-AGE score, an indicator of the degree of phenotypic ageing of the immune system. IMM-AGE was elevated in trauma patients at an early timepoint (day 3) in comparison with healthy controls (p ConclusionsThe profoundly altered peripheral adaptive immune compartment after critical injury can be used as a potential biomarker to identify individuals at a high risk of developing sepsis and this state of prematurely aged immune phenotype in biologically young individuals persists for up to two months post-hospitalisation, compromising the host immune response to infections. Reversing this aged immune system is likely to have a beneficial impact on short- and longer-term outcomes of trauma survivors.", keywords = "Traumatic injury, Immunesenescence, Sepsis, Inflammation", author = "Foster, {Mark A.} and Conor Bentley and Jon Hazeldine and Animesh Acharjee and Ornit Nahman and Shen-Orr, {Shai S.} and Lord, {Janet M.} and Duggal, {Niharika A.}", year = "2022", month = dec, day = "5", doi = "10.1186/s12979-022-00317-5", language = "English", volume = "19", journal = "Immunity & Ageing", issn = "1742-4933", publisher = "Springer", number = "1", } . Immunity & Ageing.

Computer-Aided Imaging Analysis of Probe-Based Confocal Laser Endomicroscopy With Molecular Labeling and Gene Expression Identifies Markers of Response to Biological Therapy in IBD Patients: The Endo-Omics Study <head> <META HTTP-EQUIV="Refresh" CONTENT="0;URL=/servlet/useragent"> </head> . Inflammatory Bowel Diseases.

Computer-Aided Imaging Analysis of Probe-Based Confocal Laser Endomicroscopy With Molecular Labeling and Gene Expression Identifies Markers of Response to Biological Therapy in IBD Patients @article{89b9757d4143451eb9a5928a29eed177, title = "Computer-Aided Imaging Analysis of Probe-Based Confocal Laser Endomicroscopy With Molecular Labeling and Gene Expression Identifies Markers of Response to Biological Therapy in IBD Patients: The Endo-Omics Study", abstract = "BackgroundWe aimed to predict response to biologics in inflammatory bowel disease (IBD) using computerized image analysis of probe confocal laser endomicroscopy (pCLE) in vivo and assess the binding of fluorescent-labeled biologics ex vivo. Additionally, we investigated genes predictive of anti-tumor necrosis factor (TNF) response.MethodsTwenty-nine patients (15 with Crohn{\textquoteright}s disease [CD], 14 with ulcerative colitis [UC]) underwent colonoscopy with pCLE before and 12 to 14 weeks after starting anti-TNF or anti-integrin α4β7 therapy. Biopsies were taken for fluorescein isothiocyanate–labeled infliximab and vedolizumab staining and gene expression analysis. Computer-aided quantitative image analysis of pCLE was performed. Differentially expressed genes predictive of response were determined and validated in a public cohort.ResultsIn vivo, vessel tortuosity, crypt morphology, and fluorescein leakage predicted response in UC (area under the receiver-operating characteristic curve [AUROC], 0.93; accuracy 85%, positive predictive value [PPV] 89%; negative predictive value [NPV] 75%) and CD (AUROC, 0.79; accuracy 80%; PPV 75%; NPV 83%) patients. Ex vivo, increased binding of labeled biologic at baseline predicted response in UC (UC) (AUROC, 83%; accuracy 77%; PPV 89%; NPV 50%) but not in Crohn{\textquoteright}s disease (AUROC 58%). A total of 325 differentially expressed genes distinguished responders from nonresponders, 86 of which fell within the most enriched pathways. A panel including ACTN1, CXCL6, LAMA4, EMILIN1, CRIP2, CXCL13, and MAPKAPK2 showed good prediction of anti-TNF response (AUROC >0.7).ConclusionsHigher mucosal binding of the drug target is associated with response to therapy in UC. In vivo, mucosal and microvascular changes detected by pCLE are associated with response to biologics in inflammatory bowel disease. Anti-TNF–responsive UC patients have a less inflamed and fibrotic state pretreatment. Chemotactic pathways involving CXCL6 or CXCL13 may be novel targets for therapy in nonresponders.", keywords = "ulcerative colitis, Crohn{\textquoteright}s disease, biological agents, probe confocal, laser endomicroscopy, RNA transcriptomics, laser endomicroscopy, endoscopic molecular labeling, artificial intelligence", author = "Marietta Iacucci and Louisa Jeffery and Animesh Acharjee and Enrico Grisan and Andrea Buda and Olga Nardone and Samuel Smith and Nunzia Labarile and Davide Zardo and Bella Ungar and Stuart Hunter and Ren Mao and Rosanna Cannatelli and Uday Shivaji and Parigi, {Tommaso Lorenzo} and Gary Reynolds and Georgios Gkoutos and Subrata Ghosh", year = "2022", month = nov, day = "15", doi = "10.1093/ibd/izac233", language = "English", journal = "Inflammatory Bowel Diseases", issn = "1078-0998", publisher = "Lippincott Williams and Wilkins", } . Inflammatory Bowel Diseases.

The diagnostic potential and barriers of microbiome based therapeutics <head> <META HTTP-EQUIV="Refresh" CONTENT="0;URL=/servlet/useragent"> </head> . Diagnosis.

Machine learning based attribution mapping of climate related discussions on social media @article{e197edfcacf0499eab9d110855f8fc27, title = "Machine learning based attribution mapping of climate related discussions on social media", abstract = "A united front from all the stakeholders including public, administration and academia alike is required to counter the growing threat of climate change. The recent rise of social media as the new public address system, makes it an ideal source of information to assess public discussions and responses in real time. We mine c.1.7 m posts from 55 climate related subreddits on social media platform Reddit since its inception. Using USE, a state-of-the-art sentence encoder, and K-means clustering algorithm, we develop a machine learning based approach to identify, store, process and classify the posts automatically, and at a scale. In the broad and multifaceted theme of climate change, our approach narrows down the focus to 10 critical underlying themes comprising the public discussions on social media over time. Furthermore, we employ a full order partial correlation analysis to assess the relationship between the different identified themes. We show that in line with Paris Agreement, while the climate science community has been successful in influencing the discussions on both the causes and effects of climate change, the public administration has failed to appropriately communicate the causes of climate change and has been able to influence only the discussions on the effects of it. Hence, our study shows a clear gap in the public communication by the administration, wherein counter-intuitively less emphasis has been given on the drivers of climate change. This information can be particularly beneficial to policymakers and climate activists in decision making as they try to close the gap between public and academia.", author = "Akshay Kaushal and Animesh Acharjee and Anandadeep Mandal", year = "2022", month = nov, day = "8", doi = "10.1038/s41598-022-22034-1", language = "English", volume = "12", journal = "Scientific Reports", issn = "2045-2322", publisher = "Nature Publishing Group", number = "1", } . Scientific Reports.

Integrative analysis reveals novel associations between DNA methylation and the serum metabolome of adolescents with type 2 diabetes @article{1d5aa0abd15f4348bf0942ad5afa12dd, title = "Integrative analysis reveals novel associations between DNA methylation and the serum metabolome of adolescents with type 2 diabetes: a cross-sectional study", abstract = "Objective: Rates of type 2 diabetes (T2D) among adolescents are on the rise. Epigenetic changes could be associated with the metabolic alterations in adolescents with T2D.Methods: We performed a cross sectional integrated analysis of DNA methylation data from peripheral blood mononuclear cells with serum metabolomic data from First Nation adolescents with T2D and controls participating in the Improving Renal Complications in Adolescents with type 2 diabetes through Research (iCARE) cohort study, to explore the molecular changes in adolescents with T2D.Results: Our analysis showed that 43 serum metabolites and 36 differentially methylated regions (DMR) were associated with T2D. Several DMRs were located near the transcriptional start site of genes with established roles in metabolic disease and associated with altered serum metabolites (e.g. glucose, leucine, and gamma-glutamylisoleucine). These included the free fatty acid receptor-1 (FFAR1), upstream transcription factor-2 (USF2), and tumor necrosis factor-related protein-9 (C1QTNF9), among others.Conclusions: We identified DMRs and metabolites that merit further investigation to determine their significance in controlling gene expression and metabolism which could define T2D risk in adolescents.", keywords = "Integrative analyses, multiomics, Machine Learning, Diabetes, methylation, Metabolomics", author = "Prasoon Agarwal and Wicklow, {Brandy A.} and Dart, {Allison B.} and Hizon, {Nikho A.} and Sellers, {Elizabeth A.c.} and Mcgavock, {Jonathan M.} and Talbot, {Charlotte P. J.} and Fonseca, {Mario A.} and Wayne Xu and Davie, {James R.} and Jones, {Meaghan J.} and Animesh Acharjee and Dolinsky, {Vernon W.}", year = "2022", month = oct, day = "10", doi = "10.3389/fendo.2022.934706", language = "English", volume = "13", journal = "Frontiers in Endocrinology", issn = "1664-2392", publisher = "Frontiers", } . Frontiers in Endocrinology.

Role of microbiota and microbiota‐derived short‐chain fatty acids in PDAC <head> <META HTTP-EQUIV="Refresh" CONTENT="0;URL=/servlet/useragent"> </head> . Cancer Medicine.

The diagnostic potential and barriers of microbiome based therapeutics @article{f9cad484b31e4e46a1ce544312d0b417, title = "The diagnostic potential and barriers of microbiome based therapeutics", abstract = "High throughput technological innovations in the past decade have accelerated research into the trillions of commensal microbes in the gut. The {\textquoteleft}omics{\textquoteright} technologies used for microbiome analysis are constantly evolving, and large-scale datasets are being produced. Despite of the fact that much of the research is still in its early stages, specific microbial signatures have been associated with the promotion of cancer, as well as other diseases such as inflammatory bowel disease, neurogenerative diareses etc. It has been also reported that the diversity of the gut microbiome influences the safety and efficacy of medicines. The availability and declining sequencing costs has rendered the employment of RNA-based diagnostics more common in the microbiome field necessitating improved data-analytical techniques so as to fully exploit all the resulting rich biological datasets, while accounting for their unique characteristics, such as their compositional nature as well their heterogeneity and sparsity. As a result, the gut microbiome is increasingly being demonstrating as an important component of personalised medicine since it not only plays a role in inter-individual variability in health and disease, but it also represents a potentially modifiable entity or feature that may be addressed by treatments in a personalised way. In this context, machine learning and artificial intelligence-based methods may be able to unveil new insights into biomedical analyses through the generation of models that may be used to predict category labels, and continuous values. Furthermore, diagnostic aspects will add value in the identification of the non invasive markers in the critical diseases like cancer.", keywords = "biomarker, microbiota, Machine learning;, Diagnostics", author = "Animesh Acharjee and Utpreksha Singh and Choudhury, {Saptamita Paul} and Georgios Gkoutos", note = "Funding Information: Research funding: The authors acknowledge support from the NIHR Birmingham ECMC, NIHR Birmingham SRMRC, Nanocommons H2020-EU (731032), MAESTRIA (Grant agreement ID 965286) and the MRC Heath Data Research UK (HDRUK/CFC/01), an initiative funded by UK Research and Innovation, Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, the Medical Research Council or the Department of Health. Publisher Copyright: {\textcopyright} 2022 the author(s), published by De Gruyter, Berlin/Boston 2022.", year = "2022", month = aug, day = "25", doi = "10.1515/dx-2022-0052", language = "English", journal = "Diagnosis", issn = "2194-802X", } . Diagnosis.

Differential benefits of steroid therapies in adults following major burn injury @article{480404aa35ee4c88ad4b254994509ff6, title = "Differential benefits of steroid therapies in adults following major burn injury", abstract = "Background: Major thermal injury induces a complex pathophysiological state characterized by burn shock and hypercatabolism. Steroids are used to modulate these post-injury responses. However, the effects of steroids on acute post-burn outcomes remain unclear. Methods: In this study of 52 thermally injured adult patients (median total burn surface area 42%, 33 males and 19 females), the effects of corticosteroid and oxandrolone on mortality, multi-organ failure (MOF), and sepsis were assessed individually. Clinical data were collected at days 1, 3, 7, and 14 post-injury. Results: Twenty-two (42%) and 34 (65%) burns patients received corticosteroids and oxandrolone within the same cohort, respectively. Following separate analysis for each steroid, corticosteroid use was associated with increased odds of in-hospital mortality (OR 3.25, 95% CI: 1.32–8•00), MOF (OR 2.36, 95% CI: 1.00–1.55), and sepsis (OR 5.95, 95% CI: 2.53–14.00). Days alive (HR 0.32, 95% CI: 0.18–0.60) and sepsis-free days (HR 0.54, 95% CI: 0.37–0.80) were lower among corticosteroid-treated patients. Oxandrolone use was associated with reduced odds of 28-day mortality (OR 0.11, 95% CI: 0.04–0.30), in-hospital mortality (OR 0.19, 95% CI: 0.08–0.43), and sepsis (OR 0.24, 95% CI: 0.08–0.69). Days alive, at 28 days (HR 6.42, 95% CI: 2.77–14.9) and in-hospital (HR 3.30, 95% CI: 1.93–5.63), were higher among the oxandrolone-treated group. However, oxandrolone was associated with increased MOF odds (OR 7.90, 95% CI: 2.89–21.60) and reduced MOF-free days (HR 0.23, 95% CI: 0.11–0.50). Conclusion: Steroid therapies following major thermal injury may significantly affect patient prognosis. Oxandrolone was associated with better outcomes except for MOF. Adverse effects of corticosteroids and oxandrolone should be considered when managing burn patients.", keywords = "Corticosteroids, Oxandrolone, Mortality, Multiorgan failure, Sepsis, Burns, Multi-organ failure", author = "Khaled Altarrah and Poh Tan and Animesh Acharjee and Jon Hazeldine and Barbara Torlinska and Yvonne Wilson and Tomasz Torlinski and Naiem Moiemen and Janet Lord", note = "Funding Information: Funding for the delivery of the study is supported by the Chancellor using LIBOR funds via a grant obtained by The Scar Free Foundation, UK. KA was funded by Ministry of Health and Civil Service Commission, Kuwait. JH was funded by NIHR Surgical Reconstruction and Microbiology Research centre, UK. The funders had no role in the study design, data collection, data analysis, data interpretation, drafting the report, or decision to submit the manuscript for publication. Ethical approval for the SIFTI study was granted by the National Research Ethics Service Committee East Midlands, UK (Reference 12/EM/0432). KA, NM, JML, YW, and TT were responsible for the conception and design of this study. KA, PT, and JH contributed to data collection. KA, AA, and BT conducted the statistical analyses. All authors contributed to data interpretation and manuscript preparation. All authors critically reviewed and approved the final version. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication. The manuscript was prepared according to STROBE guidelines. We thank all the study participants and their families, as well as the clinical research nurses and fellows for their support in this study. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.", year = "2022", month = aug, day = "17", doi = "10.1016/j.bjps.2022.04.007", language = "English", volume = "75", pages = "2616--2624", journal = "Journal of Plastic, Reconstructive & Aesthetic Surgery", issn = "1748-6815", publisher = "Elsevier", number = "8", } . Journal of Plastic, Reconstructive & Aesthetic Surgery.

Animesh Acharjee, Khaled Altarrah, Poh Tan, Jon Hazeldine, Barbara Torlinska, Yvonne Wilson, Tomasz Torlinski, Naiem Moiemen, Janet M Lord (2022). Differential benefits of steroid therapies in adults following major burn injury . Journal of Plastic, Reconstructive & Aesthetic Surgery.

Hypoxanthine is a pharmacodynamic marker of ischemic brain edema modified by glibenclamide @article{e01f4771221042afbd034d0805bd2cca, title = "Hypoxanthine is a pharmacodynamic marker of ischemic brain edema modified by glibenclamide", abstract = "Brain edema after a large stroke causes significant morbidity and mortality. Here, we seek to identify pharmacodynamic markers of edema that are modified by intravenous (i.v.) glibenclamide (glyburide; BIIB093) treatment. Using metabolomic profiling of 399 plasma samples from patients enrolled in the phase 2 Glyburide Advantage in Malignant Edema and Stroke (GAMES)-RP trial, 152 analytes are measured using liquid chromatography-tandem mass spectrometry. Associations with midline shift (MLS) and the matrix metalloproteinase-9 (MMP-9) level that are further modified by glibenclamide treatment are compared with placebo. Hypoxanthine is the only measured metabolite that associates with MLS and MMP-9. In sensitivity analyses, greater hypoxanthine levels also associate with increased net water uptake (NWU), as measured on serial head computed tomography (CT) scans. Finally, we find that treatment with i.v. glibenclamide reduces plasma hypoxanthine levels across all post-treatment time points. Hypoxanthine, which has been previously linked to inflammation, is a biomarker of brain edema and a treatment response marker of i.v. glibenclamide treatment.", keywords = "stroke, brain edema, glibenclamide, metabolomics, inflammation, hypoxanthine, metabolism", author = "Irvine, {Hannah J.} and Animesh Acharjee and Zoe Wolcott and Zsuzsanna Ament and H.e. Hinson and Molyneaux, {Bradley J.} and Simard, {J. Marc} and Sheth, {Kevin N.} and Kimberly, {W. Taylor}", year = "2022", month = jun, day = "21", doi = "10.1016/j.xcrm.2022.100654", language = "English", journal = "Cell Reports Medicine", issn = "2666-3791", publisher = "Cell Press", } . Cell Reports Medicine.

Animesh Acharjee, Basak Bahcivanci, Roshan Shafiha, Georgios V. Gkoutos (2022). Associating transcriptomics data with inflammatory markers to understand tumour microenvironment in hepatocellular carcinoma . Cancer Medicine.

Associating transcriptomics data with inflammatory markers to understand tumour microenvironment in hepatocellular carcinoma @article{e57651c86efd453591bdf77c5bd0cbc7, title = "Associating transcriptomics data with inflammatory markers to understand tumour microenvironment in hepatocellular carcinoma", abstract = "Background: Liver cancer is the fourth leading cause of cancer-related death globally which is estimated to reach more than 1 million deaths a year by 2030. Among liver cancer types, hepatocellular carcinoma (HCC) accounts for approximately 90% of the cases and is known to have a tumour promoting inflammation regardless of its underlying aetiology. However, current promising treatment approaches, such as immunotherapy, are partially effective for most of the patients due to the immunosuppressive nature of the tumour microenvironment (TME). Therefore, there is an urgent need to fully understand TME in HCC and discover new immune markers to eliminate resistance to immunotherapy. Methods: We analyse three microarray datasets, using unsupervised and supervised methods, in an effort to discover signature genes. First, univariate, and multivariate, feature selection methods, such as the Boruta algorithm, are applied. Subsequently, an optimisation procedure, which utilises random forest algorithm with three dataset pairs combinations, is performed. The resulting optimal gene sets are then combined and further subjected to network analysis and pathway enrichment analysis so as to obtain information related to their biological relevance. The microarray datasets were analysed via the MCP-counter, CIBERSORT, TIMER, EPIC, and quanTIseq deconvolution methods and an estimation of cell type abundances for each dataset sample were identified. The differences in the cell type abundances, between the adjacent and tumour sample groups, were then assessed using a Wilcoxon Rank Sum test (p-value < 0.05). Results: The optimal gene signature sets, derived from each of the data pairs combination, achieved AUC values ranging from 0.959 to 0.988 in external validation sets using Random Forest model. CLEC1B and PTTG1 genes are retrieved across each optimal set. Among the signature genes, PTTG1, AURKA, and UBE2C genes are found to be involved in the regulation of mitotic sister chromatid separation and anaphase-promoting complex (APC) dependent catabolic process (adjusted p-value < 0.001). Additionally, the application of deconvolution algorithms revealed significant changes in cell type abundances of Regulatory T (Treg) cells, M0 and M1 macrophages, and T CD8 + cells between adjacent and tumour samples. Conclusion: We identified ECM1 gene as a potential immune-related marker acting through immune cell migration and macrophage polarisation. Our results indicate that macrophages, such as M0 macrophage and M1 macrophage cells, undergo significant changes in HCC TME. Moreover, our immune deconvolution approach revealed significant infiltration of Treg cells and M0 macrophages, and a significant decrease in T CD8 + cells and M1 macrophages in tumour samples. ", keywords = "gene signature, hepatocellular carcinoma, immune deconvolution, tumor microenvironment", author = "Basak Bahcivanci and Roshan Shafiha and Gkoutos, {Georgios V} and Animesh Acharjee", note = "Funding Information: Animesh Acharjeeand Georgios V. Gkoutosacknowledge support from the NIHR Birmingham ECMC, NIHR Birmingham SRMRC, Nanocommons H2020‐EU (731032) and the NIHR Birmingham Biomedical Research Centre, and the MRC Health Data Research UK (HDRUK/CFC/01), an initiative funded by UK Research and Innovation, Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, the Medical Research Council or the Department of Health. Publisher Copyright: {\textcopyright} 2022 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.", year = "2022", month = jun, day = "18", doi = "10.1002/cam4.4941", language = "English", journal = "Cancer Medicine", issn = "20457634", publisher = "John Wiley & Sons", } . Cancer Medicine.

Animesh Acharjee, Hannah J. Irvine, Zoe Wolcott, Zsuzsanna Ament, H.E. Hinson, Bradley J. Molyneaux, J. Marc Simard, Kevin N. Sheth, W. Taylor Kimberly (2022). Hypoxanthine is a pharmacodynamic marker of ischemic brain edema modified by glibenclamide . Cell Reports Medicine.

Gut dysbiosis in ocular mucous membrane pemphigoid @article{ba90dd1d645942a99e6735048f4175d1, title = "Gut dysbiosis in ocular mucous membrane pemphigoid", abstract = "Mucous Membrane Pemphigoid is an orphan multi-system autoimmune scarring disease involving mucosal sites, including the ocular surface (OcMMP) and gut. Loss of tolerance to epithelial basement membrane proteins and generation of autoreactive T cell and/or autoantibodies are central to the disease process. The gut microbiome plays a critical role in the development of the immune system. Alteration in the gut microbiome (gut dysbiosis) affects the generation of autoreactive T cells and B cell autoantibody repertoire in several autoimmune conditions. This study examines the relationship between gut microbiome diversity and ocular inflammation in patients with OcMMP by comparing OcMMP gut microbiome profiles with healthy controls. DNA was extracted from faecal samples (49 OcMMP patients, 40 healthy controls), amplified for the V4 region of the 16S rRNA gene and sequenced using Illumina Miseq platform. Sequencing reads were processed using the bioinformatics pipeline available in the mothur v.1.44.1 software. After adjusting for participant factors in the multivariable model (age, gender, BMI, diet, proton pump inhibitor use), OcMMP cohort was found to be associated with lower number of operational taxonomic units (OTUs) and Shannon Diversity Index when compared to healthy controls. Within the OcMMP cohort, the number of OTUs were found to be significantly correlated with both the bulbar conjunctival inflammation score (p=0.03) and the current use of systemic immunotherapy (p=0.02). The linear discriminant analysis effect size scores indicated that Streptococcus and Lachnoclostridium were enriched in OcMMP patients whilst Oxalobacter, Clostridia uncultured genus-level group (UCG) 014, Christensenellaceae R-7 group and butyrate-producing bacteria such as Ruminococcus, Lachnospiraceae, Coprococcus, Roseburia, Oscillospiraceae UCG 003, 005, NK4A214 group were enriched in healthy controls (Log10 LDA score < 2, FDR-adjusted p <0.05). In conclusion, OcMMP patients have gut dysbiosis correlating with bulbar conjunctival inflammation and the use of systemic immunotherapies. This provides a framework for future longitudinal deep phenotyping studies on the role of the gut microbiome in the pathogenesis of OcMMP.", keywords = "ocular mucous membrane pemphigoid (OcMMP), gut microbiome, inflammation, eye, 16S sequencing", author = "Liying Low and Kusy Suleiman and Mohith Shamdas and Kerolos Bassilious and Natraj Poonit and Rossiter, {Amanda E.} and Animesh Acharjee and Nicholas Loman and Murray, {Philip I.} and Wallace, {Graham R.} and Saaeha Rauz", note = "Funding Information: LL is funded through a Fight for Sight Clinical Research Fellowship (Ref 1840/1841) and a National Institute for Health Research (NIHR) Clinical Lectureship. Publisher Copyright: Copyright {\textcopyright} 2022 Low, Suleiman, Shamdas, Bassilious, Poonit, Rossiter, Acharjee, Loman, Murray, Wallace and Rauz.", year = "2022", month = apr, day = "14", doi = "10.3389/fcimb.2022.780354", language = "English", volume = "12", journal = "Frontiers in cellular and infection microbiology", issn = "2235-2988", publisher = "Frontiers Media S.A.", } . Frontiers in cellular and infection microbiology.

Animesh Acharjee, Pedro Mena, Claudia Favari, Saisakul Chernbumroong, Letizia Bresciani, Claudio Curti, Furio Brighenti, Christian Heiss, Ana Rodriguez-Mateos, Daniele Del Rio(2022). Metabotypes of flavan-3-ol colonic metabolites after cranberry intake: elucidation and statistical approaches . European Journal of Nutrition. 61. (3). p. 1299--1317. Springer Science and Business Media {LLC}

Animesh Acharjee, Yuanwei Xu, Katrina Nash, Georgios V Gkoutos, Jonathan Wren(2022). CACONET: a novel classification framework for microbial correlation networks . Bioinformatics. 38. (6). p. 1639--1647. Oxford University Press ({OUP})

Iron-mediated epigenetic activation of NRF2 targets @article{3a851751baef4648b42f107836ca8b49, title = "Iron-mediated epigenetic activation of NRF2 targets", abstract = "The toxic effects of excess dietary iron within the colonic lumen are well documented, particularly in the context of Inflammatory Bowel Disease (IBD) and Colorectal Cancer (CRC). Proposed mechanisms that underpin iron-associated intestinal disease include: i) the pro-inflammatory and ROS-promoting nature of iron, ii) gene-expression alterations, and iii) intestinal microbial dysbiosis. However, to date no studies have examined the effect of iron on the colonic epigenome. Here we demonstrate that chronic iron exposure of colonocytes leads to significant hypomethylation of the epigenome. Bioinformatic analysis highlights a significant epigenetic effect on NRF2 (nuclear factor erythroid 2-related factor 2) pathway targets (including NAD(P)H Quinone Dehydrogenase 1 [NQO1] and Glutathione peroxidase 2 [GPX2]); this demethylating effect was validated and subsequent gene and protein expression quantified. These epigenetic modifications were not observed upon the diminishment of cellular lipid peroxidation with endogenous glutathione and the subsequent removal of iron. Additionally, the induction of TET1 expression was found post-iron treatment, highlighting the possibility of an oxidative-stress induction of TET1 and subsequent hypomethylation of NRF2 targets. In addition, a strong time dependence on the establishment of iron-orchestrated hypomethylation was found which was concurrent with the increase in the intracellular labile iron pool (LIP) and lipid peroxidation levels. These epigenetic changes were further validated in murine intestinal mucosa in models administered a chronic iron diet, providing evidence for the likelihood of dietary-iron mediated epigenetic alterations in vivo. Furthermore, significant correlations were found between NQO1 and GPX2 demethylation and human intestinal tissue iron-status, thus suggesting that these iron-mediated epigenetic modifications are likely in iron-replete enterocytes. Together, these data describe a novel mechanism by which excess dietary iron is able to alter the intestinal phenotype, which could have implications in iron-mediated intestinal disease and the regulation of ferroptosis.", keywords = "Diet, Iron, Epigenome, Nutrigenetics, NRF2, Oxidative Stress, Hypomethylation", author = "Horniblow, {Richard D} and Prachi Pathak and Balacco, {Dario L} and Animesh Acharjee and Eva Lles and Georgios Gkoutos and Beggs, {Andrew D} and Chris Tselepis", year = "2022", month = mar, doi = "10.1016/j.jnutbio.2021.108929", language = "English", volume = "101", journal = "Journal of Nutritional Biochemistry", issn = "0955-2863", publisher = "Elsevier", } . Journal of Nutritional Biochemistry.

Animesh Acharjee, Richard D Horniblow, Prachi Pathak, Dario L Balacco, Eva Lles, Georgios Gkoutos, Andrew D Beggs, Chris Tselepis(2022). Iron-mediated epigenetic activation of NRF2 targets . The Journal of Nutritional Biochemistry. 101. p. 108929. Elsevier {BV}

Animesh Acharjee, Saraswati Koppad, Annappa Basava, Katrina Nash, Georgios V. Gkoutos (2022). Machine Learning-Based Identification of Colon Cancer Candidate Diagnostics Genes . Biology.

The potential of fecal microbiota and amino acids to detect and monitor patients with adenoma @article{5c88f2df2d064849b097dc2df723847a, title = "The potential of fecal microbiota and amino acids to detect and monitor patients with adenoma", abstract = "The risk of recurrent dysplastic colonic lesions is increased following polypectomy. Yield of endoscopic surveillance after adenoma removal is low, while interval colorectal cancers occur. To longitudinally assess the dynamics of fecal microbiota and amino acids in the presence of adenomatous lesions and after their endoscopic removal. In this longitudinal case-control study, patients collected fecal samples prior to bowel preparation before scheduled colonoscopy and 3 months after this intervention. Based on colonoscopy outcomes, patients with advanced adenomas and nonadvanced adenomas (0.5-1.0 cm) who underwent polypectomy during endoscopy (n = 19) were strictly matched on age, body-mass index, and smoking habits to controls without endoscopic abnormalities (n = 19). Microbial taxa were measured by 16S RNA sequencing, and amino acids (AA) were measured by high-performance liquid chromatography (HPLC). Adenoma patients were discriminated from controls based on AA and microbial composition. Levels of proline (p = .001), ornithine (p = .02) and serine (p = .02) were increased in adenoma patients compared to controls but decreased to resemble those of controls after adenoma removal. These AAs were combined as a potential adenoma-specific panel (AUC 0.79(0.64-0.94)). For bacterial taxa, differences between patients with adenomas and controls were found (Bifidobacterium spp.↓, Anaerostipes spp.↓, Butyricimonas spp.↑, Faecalitalea spp.↑ and Catenibacterium spp.↑), but no alterations in relative abundance were observed after polypectomy. Furthermore, Faecalitalea spp. and Butyricimonas spp. were significantly correlated with adenoma-specific amino acids. We selected an amino acid panel specifically increased in the presence of adenomas and a microbial signature present in adenoma patients, irrespective of polypectomy. Upon validation, these panels may improve the effectiveness of the surveillance program by detection of high-risk individuals and determination of surveillance endoscopy timing, leading to less unnecessary endoscopies and less interval cancer.", keywords = "omics, biomarker, surveillance, adenoma, colorectal cancer", author = "Sofie Bosch and Animesh Acharjee and Quraishi, {Mohammed N} and Patricia Rojas and Abdellatif Bakkali and Jansen, {Erwin Ew} and {Brizzio Brentar}, Marina and Johan Kuijvenhoven and Pieter Stokkers and Eduard Struys and Beggs, {Andrew D} and Gkoutos, {Georgios V} and {de Meij}, {Tim Gj} and {de Boer}, {Nanne Kh}", year = "2022", month = feb, day = "21", doi = "10.1080/19490976.2022.2038863", language = "English", volume = "14", journal = "Gut Microbes", issn = "1949-0976", publisher = "Taylor & Francis", number = "1", } . Gut Microbes.

Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models @article{ee67bb9d6e374456bec78643279d4743, title = "Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models", abstract = "BACKGROUND: Numerous approaches have been proposed for the detection of epistatic interactions within GWAS datasets in order to better understand the drivers of disease and genetics.METHODS: A selection of state-of-the-art approaches were assessed. These included the statistical tests, fast-epistasis, BOOST, logistic regression and wtest; swarm intelligence methods, namely AntEpiSeeker, epiACO and CINOEDV; and data mining approaches, including MDR, GSS, SNPRuler and MPI3SNP. Data were simulated to provide randomly generated models with no individual main effects at different heritabilities (pure epistasis) as well as models based on penetrance tables with some main effects (impure epistasis). Detection of both two and three locus interactions were assessed across a total of 1,560 simulated datasets. The different methods were also applied to a section of the UK biobank cohort for Atrial Fibrillation.RESULTS: For pure, two locus interactions, PLINK's implementation of BOOST recovered the highest number of correct interactions, with 53.9% and significantly better performing than the other methods (p = 4.52e - 36). For impure two locus interactions, MDR exhibited the best performance, recovering 62.2% of the most significant impure epistatic interactions (p = 6.31e - 90 for all but one test). The assessment of three locus interaction prediction revealed that wtest recovered the highest number (17.2%) of pure epistatic interactions(p = 8.49e - 14). wtest also recovered the highest number of three locus impure epistatic interactions (p = 6.76e - 48) while AntEpiSeeker ranked as the most significant the highest number of such interactions (40.5%). Finally, when applied to a real dataset for Atrial Fibrillation, most notably finding an interaction between SYNE2 and DTNB.", keywords = "Algorithms, Alleles, Atrial Fibrillation/genetics, Data Mining/methods, Dystrophin-Associated Proteins/genetics, Epistasis, Genetic, Gene Frequency, Genetic Loci, Genome-Wide Association Study/methods, Genotype, Humans, Linear Models, Microfilament Proteins/genetics, Models, Genetic, Multifactor Dimensionality Reduction, Nerve Tissue Proteins/genetics, Neuropeptides/genetics, Penetrance, Polymorphism, Single Nucleotide, ROC Curve", author = "Dominic Russ and Williams, {John A} and Cardoso, {Victor Roth} and Laura Bravo-Merodio and Pendleton, {Samantha C} and Furqan Aziz and Animesh Acharjee and Gkoutos, {Georgios V}", note = "Funding Information: The authors acknowledge support from the NIHR Birmingham ECMC, NIHR Birmingham SRMRC, Nanocommons H2020-EU (731032) and the NIHR Birmingham Biomedical Research Centre and the MRC Heath Data Research UK (HDRUK/CFC/01), an initiative funded by UK Research and Innovation, Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, the Medical Research Council or the Department of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Publisher Copyright: {\textcopyright} 2022 Russ et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.", year = "2022", month = feb, day = "18", doi = "10.1371/journal.pone.0263390", language = "English", volume = "17", journal = "PLoSONE", issn = "1932-6203", publisher = "Public Library of Science (PLOS)", number = "2", } . PLoS ONE.

Animesh Acharjee, Saraswati Koppad, Annappa Basava, Katrina Nash, Georgios Gkoutos PhD DIC FRSB FRSM (2022). Machine Learning-Based Identification of Colon Cancer Candidate Diagnostics Genes . Biology.

CACONET @article{4a9821dcb5264ceaa40cbe926e3beaf7, title = "CACONET: a novel classification framework for microbial correlation networks", abstract = "MOTIVATION: Existing microbiome-based disease prediction relies on the ability of machine learning methods to differentiate disease from healthy subjects based on the observed taxa abundance across samples. Despite numerous microbes have been implicated as potential biomarkers, challenges remain due to not only the statistical nature of microbiome data, but also the lack of understanding of microbial interactions which can be indicative of the disease.RESULTS: We propose CACONET (classification of Compositional-Aware COrrelation NETworks), a computational framework that learns to classify microbial correlation networks and extracts potential signature interactions, taking as input taxa relative abundance across samples and their health status. By using Bayesian compositional-aware correlation inference, a collection of posterior correlation networks can be drawn and used for graph-level classification, thus incorporating uncertainty in the estimates. CACONET then employs a deep learning approach for graph classification, achieving excellent performance metrics by exploiting the correlation structure. We test the framework on both simulated data and a large real-world dataset pertaining to microbiome samples of colorectal cancer (CRC) and healthy subjects, and identify potential network substructure characteristic of CRC microbiota. CACONET is customizable and can be adapted to further improve its utility.AVAILABILITY: CACONET is available at https://github.com/yuanwxu/corr-net-classify.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", author = "Yuanwei Xu and Katrina Nash and Animesh Acharjee and Gkoutos, {Georgios V}", note = "{\textcopyright} The Author(s) 2022. Published by Oxford University Press.", year = "2022", month = jan, day = "4", doi = "10.1093/bioinformatics/btab879", language = "English", journal = "Bioinformatics", issn = "1367-4803", publisher = "Oxford University Press", } . Bioinformatics.

Animesh Acharjee, Saptamita Paul Choudhury (2022). Artificial intelligence-based personalized nutrition and prediction of irritable bowel syndrome patients . Therapeutic Advances in Gastroenterology.

Integration of metabolomic and clinical data improves the prediction of intensive care unit length of stay following major traumatic injury @article{96ac12de0cf440db812f8a2b38b1a37b, title = "Integration of metabolomic and clinical data improves the prediction of intensive care unit length of stay following major traumatic injury", abstract = "Recent advances in emergency medicine and the co-ordinated delivery of trauma care mean more critically-injured patients now reach the hospital alive and survive life-saving operations. Indeed, between 2008 and 2017, the odds of surviving a major traumatic injury in the UK increased by nineteen percent. However, the improved survival rates of severely-injured patients have placed an increased burden on the healthcare system, with major trauma a common cause of intensive care unit (ICU) admissions that last ≥10 days. Improved understanding of the factors influencing patient outcomes is now urgently needed. We investigated the serum metabolomic profile of fifty-five major trauma patients across three post-injury phases: acute (days 0-4), intermediate (days 5-14) and late (days 15-112). Using ICU length of stay (LOS) as a clinical outcome, we aimed to determine whether the serum metabolome measured at days 0-4 post-injury for patients with an extended (≥10 days) ICU LOS differed from that of patients with a short (<10 days) ICU LOS. In addition, we investigated whether combining metabolomic profiles with clinical scoring systems would generate a variable that would identify patients with an extended ICU LOS with a greater degree of accuracy than models built on either variable alone. The number of metabolites unique to and shared across each time segment varied across acute, intermediate and late segments. A one-way ANOVA revealed the most variation in metabolite levels across the different time-points was for the metabolites lactate, glucose, anserine and 3-hydroxybutyrate. A total of eleven features were selected to differentiate between <10 days ICU LOS vs. >10 days ICU LOS. New Injury Severity Score (NISS), testosterone, and the metabolites cadaverine, urea, isoleucine, acetoacetate, dimethyl sulfone, syringate, creatinine, xylitol, and acetone form the integrated biomarker set. Using metabolic enrichment analysis, we found valine, leucine and isoleucine biosynthesis, glutathione metabolism, and glycine, serine and threonine metabolism were the top three pathways differentiating ICU LOS with a p < 0.05. A combined model of NISS and testosterone and all nine selected metabolites achieved an AUROC of 0.824. Differences exist in the serum metabolome of major trauma patients who subsequently experience a short or prolonged ICU LOS in the acute post-injury setting. Combining metabolomic data with anatomical scoring systems allowed us to discriminate between these two groups with a greater degree of accuracy than that of either variable alone.", keywords = "metabolomics, omics integration, ICU length of stay, inflammation", author = "Animesh Acharjee and Jon Hazeldine and Alina Bazarova and Lavanya Deenadayalu and Jinkang Zhang and Conor Bentley and Dominic Russ and Lord, {Janet M} and Gkoutos, {Georgios V} and Young, {Stephen P} and Foster, {Mark A}", note = "Funding Information: Funding: The SIR Study was part of the Surgeon General{\textquoteright}s Casualty Nutrition Study (SGCNS), a Ministry of Defence funded project. The detailed ongoing analysis of the cohort was funded by the National Institute for Health Research (NIHR) Surgical Reconstruction and Microbiology Research Centre (SRMRC), Birmingham. JH is supported by the National Institute for Health Research (NIHR) Surgical Reconstruction and Microbiology Research Centre (SRMRC). GVG also acknowledges support from H2020-EINFRA (731075) and the National Science Foundation (IOS:1340112) as well as support from the NIHR Birmingham ECMC, the NIHR Birmingham Biomedical Research Centre and the MRC HDR UK. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, the Medical Research Council or the Department of Health, UK. The funders provided the financial support to this research but had no role in the design of the study, analysis, interpretations of the data and in writing the manuscript. Funding Information: The SIR Study was part of the Surgeon General?s Casualty Nutrition Study (SGCNS), a Ministry of Defence funded project. The detailed ongoing analysis of the cohort was funded by the National Institute for Health Research (NIHR) Surgical Reconstruction and Microbiology Research Centre (SRMRC), Birmingham. JH is supported by the National Institute for Health Research (NIHR) Surgical Reconstruction and Microbiology Research Centre (SRMRC). GVG also acknowledges support from H2020-EINFRA (731075) and the National Science Foundation (IOS:1340112) as well as support from the NIHR Birmingham ECMC, the NIHR Birmingham Biomedical Research Centre and the MRC HDR UK. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, the Medical Research Council or the Department of Health, UK. The funders provided the financial support to this research but had no role in the design of the study, analysis, interpretations of the data and in writing the manuscript. Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.", year = "2022", month = jan, doi = "10.3390/metabo12010029", language = "English", volume = "12", journal = "Metabolites", issn = "2218-1989", publisher = "MDPI", number = "1", } . Metabolites.

Animesh Acharjee, Jon Hazeldine, Alina Bazarova, Lavanya Deenadayalu, Jinkang Zhang, Conor Bentley, Dominic Russ, Janet M. Lord, Georgios V. Gkoutos, Stephen P. Young, et al.(2021). Integration of Metabolomic and Clinical Data Improves the Prediction of Intensive Care Unit Length of Stay Following Major Traumatic Injury . Metabolites. 12. (1). p. 29. {MDPI} {AG}

Multi-omics-based identification of atopic dermatitis target genes and their potential associations with metabolites and miRNAs @article{d8d86c15744c4110b6a5a50413d69519, title = "Multi-omics-based identification of atopic dermatitis target genes and their potential associations with metabolites and miRNAs", abstract = "Atopic dermatitis (AD), or atopic eczema, is one of the most common inflammatory skin diseases with up to 10% prevalence in adults, and approximately 15-20% in children in industrialized countries. As a result, there is an unmet need for faster, safer, and effective treatments for AD. AD pathogenesis represents a complex interplay between multiple factors, such as environmental factors or stimuli, genetic factors, immune dysfunctions. However, although multi-omics label studies have been very useful in understanding the pathophysiological mechanisms of AD and its clinical manifestations, there have been very few studies that integrate different labels of omics data. Here, we attempted to integrate gene expression and metabolomics datasets from multiple different publicly available AD cohort datasets and conduct an integrated systems-level AD analysis. We used four different GEO transcriptome data sets and, by applying an elastic net machine learning algorithm, identified robust hub genes that can be used as signatures, for example, H2AFX, MCM7, ESR1 and SF3A2. Moreover, we investigated potential associations of those genes by applying a pathway-based approach over metabolomics and miRNA datasets. Our results revealed potential novel associations between fatty acids and peroxisomal lipid metabolism pathways, as well as with several microRNAs.", keywords = "Multi-omics, machine learning, atopic dermatitis (AD), eczema, pathway analysis, translational research", author = "Animesh Acharjee and Elizaveta Gribaleva and Subia Bano and Gkoutos, {Georgios V}", year = "2021", month = dec, day = "30", language = "English", volume = "13", pages = "13697--13709", journal = "American Journal of Translational Research", issn = "1943-8141", publisher = "E-Century Publishing", number = "12", }. American Journal of Translational Research.

Animesh Acharjee, Jon Hazeldine, Alina Bazarova, Lavanya Deenadayalu, Jinkang Zhang, Conor Bentley, Dominic Russ, Janet M. Lord, Georgios Gkoutos PhD DIC FRSB FRSM, Stephen P. Young, et al. (2021). Integration of Metabolomic and Clinical Data Improves the Prediction of Intensive Care Unit Length of Stay Following Major Traumatic Injury . Metabolites.

A multi-factorial observational study on sequential fecal microbiota transplant in patients with medically refractory Clostridioides difficile infection @article{0a1e322aa48745749341bbf7cc6c5c6e, title = "A multi-factorial observational study on sequential fecal microbiota transplant in patients with medically refractory Clostridioides difficile infection", abstract = "Fecal microbiota transplantation (FMT) is highly effective in recurrent Clostridioides difficile infection (CDI); increasing evidence supports FMT in severe or fulminant Clostridioides difficile infection (SFCDI). However, the multifactorial mechanisms that underpin the efficacy of FMT are not fully understood. Systems biology approaches using high-throughput technologies may help with mechanistic dissection of host-microbial interactions. Here, we have undertaken a deep phenomics study on four adults receiving sequential FMT for SFCDI, in which we performed a longitudinal, integrative analysis of multiple host factors and intestinal microbiome changes. Stool samples were profiled for changes in gut microbiota and metabolites and blood samples for alterations in targeted epigenomic, metabonomic, glycomic, immune proteomic, immunophenotyping, immune functional assays, and T-cell receptor (TCR) repertoires, respectively. We characterised temporal trajectories in gut microbial and host immunometabolic data sets in three responders and one non-responder to sequential FMT. A total of 562 features were used for analysis, of which 78 features were identified, which differed between the responders and the non-responder. The observed dynamic phenotypic changes may potentially suggest immunosenescent signals in the non-responder and may help to underpin the mechanisms accompanying successful FMT, although our study is limited by a small sample size and significant heterogeneity in patient baseline characteristics. Our multi-omics integrative longitudinal analytical approach extends the knowledge regarding mechanisms of efficacy of FMT and highlights preliminary novel signatures, which should be validated in larger studies.", keywords = "fecal microbiota transplantation, Clostridioides difficile, immunosenescence, host-microbial interactions, systems biology", author = "Monaghan, {Tanya M.} and Duggal, {Niharika A.} and Elisa Rosati and Ruth Griffin and Jamie Hughes and Brandi Roach and Yang, {David Y.} and Christopher Wang and Karen Wong and Lynora Saxinger and Maja Pu{\v c}i{\'c}-bakovi{\'c} and Frano Vu{\v c}kovi{\'c} and Filip Klicek and Gordan Lauc and Paddy Tighe and Mullish, {Benjamin H.} and Blanco, {Jesus Miguens} and Mcdonald, {Julie A. K.} and Marchesi, {Julian R.} and Ning Xue and Tania Dottorini and Animesh Acharjee and Andre Franke and Yingrui Li and Wong, {Gane Ka-shu} and Christos Polytarchou and Yau, {Tung On} and Niki Christodoulou and Maria Hatziapostolou and Minkun Wang and Russell, {Lindsey A.} and Kao, {Dina H.}", year = "2021", month = nov, day = "19", doi = "10.3390/cells10113234", language = "English", volume = "10", journal = "Cells", issn = "2073-4409", publisher = "Multidisciplinary Digital Publishing Institute (MDPI)", number = "11", } . Cells.

Metabotypes of flavan-3-ol colonic metabolites after cranberry intake @article{b87455cc46594382945500101dc4e756, title = "Metabotypes of flavan-3-ol colonic metabolites after cranberry intake: elucidation and statistical approaches", abstract = "Purpose: Extensive inter-individual variability exists in the production of flavan-3-ol metabolites. Preliminary metabolic phenotypes (metabotypes) have been defined, but there is no consensus on the existence of metabotypes associated with the catabolism of catechins and proanthocyanidins. This study aims at elucidating the presence of different metabotypes in the urinary excretion of main flavan-3-ol colonic metabolites after consumption of cranberry products and at assessing the impact of the statistical technique used for metabotyping.Methods: Data on urinary concentrations of phenyl-γ-valerolactones and 3-(hydroxyphenyl)propanoic acid derivatives from two human interventions has been used. Different multivariate statistics, principal component analysis (PCA), cluster analysis, and partial least square-discriminant analysis (PLS-DA), have been considered.Results: Data pre-treatment plays a major role on resulting PCA models. Cluster analysis based on k-means and a final consensus algorithm lead to quantitative-based models, while the expectation–maximization algorithm and clustering according to principal component scores yield metabotypes characterized by quali-quantitative differences in the excretion of colonic metabolites. PLS-DA, together with univariate analyses, has served to validate the urinary metabotypes in the production of flavan-3-ol metabolites and to confirm the robustness of the methodological approach.Conclusions: This work proposes a methodological workflow for metabotype definition and highlights the importance of data pre-treatment and clustering methods on the final outcomes for a given dataset. It represents an additional step toward the understanding of the inter-individual variability in flavan-3-ol metabolism.Trial registration: The acute study was registered at clinicaltrials.gov as NCT02517775, August 7, 2015; the chronic study was registered at clinicaltrials.gov as NCT02764749, May 6, 2016.", keywords = "Metabotypes, Flavan-3-ols, Inter-individual variation, Phenolic metabolites, Phenyl-γ-valerolactones", author = "Pedro Mena and Claudia Favari and Animesh Acharjee and Saisakul Chernbumroong and Letizia Bresciani and Claudio Curti and Furio Brighenti and Christian Heiss and Ana Rodriguez-mateos and {Del Rio}, Daniele", year = "2021", month = nov, day = "9", doi = "10.1007/s00394-021-02692-z", language = "English", journal = "European Journal of Nutrition", issn = "1436-6207", publisher = "D. Steinkopff-Verlag", } . European Journal of Nutrition.

Animesh Acharjee, Roshan Shafiha, Basak Bahcivanci, Georgios V. Gkoutos(2021). Machine Learning-Based Identification of Potentially Novel Non-Alcoholic Fatty Liver Disease Biomarkers . Biomedicines. 9. (11). p. 1636. {MDPI} {AG}

Machine learning-based identification of potentially novel non-alcoholic fatty liver disease biomarkers @article{10cb57eaad8741ecb254686436abe55c, title = "Machine learning-based identification of potentially novel non-alcoholic fatty liver disease biomarkers", abstract = "Non-alcoholic fatty liver disease (NAFLD) is a chronic liver disease that presents a great challenge for treatment and prevention. This study aims to implement a machine learning approach that employs such datasets to identify potential biomarker targets. We developed a pipeline to identify potential biomarkers for NAFLD that includes five major processes, namely, a pre-processing step, a feature selection and a generation of a random forest model and, finally, a downstream feature analysis and a provision of a potential biological interpretation. The pre-processing step includes data normalising and variable extraction accompanied by appropriate annotations. A feature selection based on a differential gene expression analysis is then conducted to identify significant features and then employ them to generate a random forest model whose performance is assessed based on a receiver operating characteristic curve. Next, the features are subjected to a downstream analysis, such as univariate analysis, a pathway enrichment analysis, a network analysis and a generation of correlation plots, boxplots and heatmaps. Once the results are obtained, the biological interpretation and the literature validation is conducted over the identified features and results. We applied this pipeline to transcriptomics and lipidomic datasets and concluded that the C4BPA gene could play a role in the development of NAFLD. The activation of the complement pathway, due to the downregulation of the C4BPA gene, leads to an increase in triglyceride content, which might further render the lipid metabolism. This approach identified the C4BPA gene, an inhibitor of the complement pathway, as a potential biomarker for the development of NAFLD", keywords = "NAFLD, biomarker, machine learning, transcriptomics, lipidomics", author = "Roshan Shafiha and Basak Bahcivanci and Georgios Gkoutos and Animesh Acharjee", year = "2021", month = nov, day = "7", doi = "10.3390/biomedicines9111636", language = "English", volume = "9", journal = "Biomedicines", issn = "2227-9059", publisher = "MDPI", number = "11", } . Biomedicines.

Animesh Acharjee, Roshan Shafiha, Basak Bahcivanci, Georgios Gkoutos PhD DIC FRSB FRSM (2021). Machine Learning-Based Identification of Potentially Novel Non-Alcoholic Fatty Liver Disease Biomarkers . Biomedicines.

Animesh Acharjee, Tanya M. Monaghan, Niharika A Duggal, Elisa Rosati, Ruth Griffin, Jamie Hughes, Brandi Roach, David Y. Yang, Christopher Wang, Karen Wong, et al. (2021). A Multi-Factorial Observational Study on Sequential Fecal Microbiota Transplant in Patients with Medically Refractory <i>Clostridioides difficile</i> Infection . Cells.

(2021). Immune infiltration and prognostic and diagnostic use of LGALS4 in colon adenocarcinoma and bladder urothelial carcinoma . American journal of translational research.

Immune infiltration and prognostic and diagnostic use of LGALS4 in colon adenocarcinoma and bladder urothelial carcinoma @article{dd81ca004ba4468f8e91acc8ccb32744, title = "Immune infiltration and prognostic and diagnostic use of LGALS4 in colon adenocarcinoma and bladder urothelial carcinoma", abstract = "Colon adenocarcinoma (COAD) is a common tumor of the gastrointestinal tract with a high mortality rate. Current research has identified many genes associated with immune infiltration that play a vital role in the development of COAD. In this study, we analyzed the prognostic and diagnostic features of such immune-related genes in the context of colonic adenocarcinoma (COAD). We analysed 17 overlapping gene expression profiles of COAD and healthy samples obtained from TCGA-COAD and public single-cell sequencing resources, to identify potential therapeutic COAD targets. We evaluated the abundance of immune infiltration with those genes using the TIMER (Tumor Immune Estimation Resource) deconvolution method. Subsequently, we developed predictive and survival models to assess the prognostic value of these genes. The LGALS4 (Galectin-4) gene was found to be significantly (P<0.05) downregulated in COAD and bladder urothelial carcinoma (BLCA) compared to healthy samples. We identified LGALS4 as a prognostic and diagnostic marker for multiple cancer types, including COAD and BLCA. Our analysis reveals a series of novel candidate drug targets, as well as candidate molecular markers, that may explain the pathogenesis of COAD and BLCA. LGALS4 gene is associated with multiple cancer types and is a possible prognostic, as well as diagnostic, marker of COAD and BLCA.", keywords = "Biomarker, BLCA, Immune infiltration, LGALS4, Omics integration, Translational research", author = "Animesh Acharjee and Prasoon Agarwal and Katrina Nash and Subia Bano and Taufiq Rahman and Georgios Gkoutos", note = "Publisher Copyright: {\textcopyright} 2021 E-Century Publishing Corporation. All rights reserved.", year = "2021", month = oct, day = "30", language = "English", volume = "13", pages = "11353--11363", journal = "American Journal of Translational Research", issn = "1943-8141", publisher = "E-Century Publishing", number = "10", }. American Journal of Translational Research.

Animesh Acharjee, Marietta Iacucci, Louisa Jeffery, Olga Maria Nardone, Davide Zardo, Samuel C L Smith, Alina Bazarova, Rosanna Cannatelli, Uday N Shivaji, John Williams, et al.(2021). Ultra-high Magnification Endocytoscopy and Molecular Markers for Defining Endoscopic and Histologic Remission in Ulcerative Colitis—An Exploratory Study to Define Deep Remission . Inflammatory Bowel Diseases. Oxford University Press ({OUP})

Multimorbidity prediction using link prediction @article{1f421d85990e4e3985a7b6b59659b578, title = "Multimorbidity prediction using link prediction", abstract = "Multimorbidity, frequently associated with aging, can be operationally defined as the presence of two or more chronic conditions. Predicting the likelihood of a patient with multimorbidity to develop a further particular disease in the future is one of the key challenges in multimorbidity research. In this paper we are using a network-based approach to analyze multimorbidity data and develop methods for predicting diseases that a patient is likely to develop. The multimorbidity data is represented using a temporal bipartite network whose nodes represent patients and diseases and a link between these nodes indicates that the patient has been diagnosed with the disease. Disease prediction then is reduced to a problem of predicting those missing links in the network that are likely to appear in the future. We develop a novel link prediction method for static bipartite network and validate the performance of the method on benchmark datasets. By using a probabilistic framework, we then report on the development of a method for predicting future links in the network, where links are labelled with a time-stamp. We apply the proposed method to three different multimorbidity datasets and report its performance measured by different performance metrics including AUC, Precision, Recall, and F-Score.", author = "Furqan Aziz and {Roth Cardoso}, Victor and Laura Bravo-Merodio and Dominic Russ and Samantha Pendleton and John Williams and Animesh Acharjee and Georgios Gkoutos", note = "Funding Information: The authors acknowledge support from the NIHR Birmingham ECMC, NIHR Birmingham SRMRC, Nanocom-mons H2020-EU (731032) and the NIHR Birmingham Biomedical Research Centre and the MRC Heath Data Research UK (HDRUK/CFC/01), an initiative funded by UK Research and Innovation, Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, the Medical Research Council or the Department of Health. Publisher Copyright: {\textcopyright} 2021, The Author(s).", year = "2021", month = aug, day = "12", doi = "10.1038/s41598-021-95802-0", language = "English", volume = "11", journal = "Scientific Reports", issn = "2045-2322", publisher = "Nature Publishing Group", number = "1", } . Scientific Reports.

Animesh Acharjee, Hannah Wood, Hayden Pearce, Mohammed Nabil Quraishi, Richard Powell, Amanda Rossiter, Andrew Beggs, Andrew Ewer, Paul Moss, Gergely Toldi (2021). Breastfeeding promotes early neonatal regulatory T‐cell expansion and immune tolerance of non‐inherited maternal antigens . Allergy.

NFnetFu: A novel workflow for microbiome data fusion <head> <META HTTP-EQUIV="Refresh" CONTENT="0;URL=/servlet/useragent"> </head> . Computers in Biology and Medicine.

NFnetFu @article{fd400aaa427649cc99ef7c8782f53ab7, title = "NFnetFu: A novel workflow for microbiome data fusion", abstract = "Microbiome data analysis and its interpretation into meaningful biological insights remain very challenging for numerous reasons, perhaps most prominently, due to the need to account for multiple factors, including collinearity, sparsity (excessive zeros) and effect size, that the complex experimental workflow and subsequent downstream data analysis require. Moreover, a meaningful microbiome data analysis necessitates the development of interpretable models that incorporate inferences across available data as well as background biomedical knowledge. We developed a multimodal framework that considers sparsity (excessive zeros), lower effect size, intrinsically microbial correlations, i.e., collinearity, as well as background biomedical knowledge in the form of a cluster-infused enriched network architecture. Finally, our framework also provides a candidate taxa/Operational Taxonomic Unit (OTU) that can be targeted for future validation experiments. We have developed a tool, the term NFnetFU (Neuro Fuzzy network Fusion), that encompasses our framework and have made it freely available at https://github.com/VartikaBisht6197/NFnetFu.", keywords = "Microbiome, Fuzzy inference, Clustering, Network fusion", author = "Vartika Bisht and Animesh Acharjee and Gkoutos, {Georgios V}", year = "2021", month = aug, doi = "10.1016/j.compbiomed.2021.104556", language = "English", volume = "135", journal = "Computers in biology and medicine", issn = "0010-4825", publisher = "Elsevier", } . Computers in Biology and Medicine.

Animesh Acharjee, John A. Williams, Dominic Russ, Laura Bravo-Merodio, Victor Roth Cardoso, Samantha C. Pendleton, Furqan Aziz, Georgios V. Gkoutos (2021). A Causal Web between Chronotype and Metabolic Health Traits . Genes.

A causal web between chronotype and metabolic health traits @article{45a7ae0fbba44477805c716e3ff610a4, title = "A causal web between chronotype and metabolic health traits", abstract = "Observational and experimental evidence has linked chronotype to both psychological and cardiometabolic traits. Recent Mendelian randomization (MR) studies have investigated direct links between chronotype and several of these traits, often in isolation of outside potential mediating or moderating traits. We mined the EpiGraphDB MR database for calculated chronotype–trait associations (p-value < 5 × 10−8). We then re-analyzed those relevant to metabolic or mental health and investigated for statistical evidence of horizontal pleiotropy. Analyses passing multiple testing correction were then investigated for confounders, colliders, intermediates, and reverse intermediates using the EpiGraphDB database, creating multiple chronotype–trait interactions among each of the the traits studied. We revealed 10 significant chronotype–exposure associations (false discovery rate < 0.05) exposed to 111 potential previously known confounders, 52 intermediates, 18 reverse intermediates, and 31 colliders. Chronotype–lipid causal associations collided with treatment and diabetes effects; chronotype–bipolar associations were mediated by breast cancer; and chronotype–alcohol intake associations were impacted by confounders and intermediate variables including known zeitgebers and molecular traits. We have reported the influence of chronotype on several cardiometabolic and behavioural traits, and identified potential confounding variables not reported on in studies while discovering new associations to drugs and disease.", keywords = "circadian rhythm, chronotype, diabetes, alcohol intake, bipolar disorder, mendelian randomization", author = "Williams, {John A.} and Dominic Russ and Merodio, {Laura Bravo} and Cardoso, {Victor Roth} and Samantha Pendleton and Furqan Aziz and Animesh Acharjee and Georgios Gkoutos", year = "2021", month = jul, day = "1", doi = "10.3390/genes12071029", language = "English", volume = "12", journal = "Genes", issn = "2073-4425", publisher = "MDPI", number = "7", } . Genes.

Animesh Acharjee, Tanya M. Monaghan, Rima N. Biswas, Rupam R. Nashine, Samidha S. Joshi, Benjamin Mullish, Anna M. Seekatz, Jesus Miguens Blanco, Julie A. K. McDonald, Julian Marchesi, et al. (2021). Multiomics Profiling Reveals Signatures of Dysmetabolism in Urban Populations in Central India . Microorganisms.

Animesh Acharjee, John A. Williams, Dominic Russ, Laura Bravo Merodio, Victor Roth Cardoso, Samantha Pendleton, Furqan Aziz, Georgios Gkoutos PhD DIC FRSB FRSM (2021). A Causal Web between Chronotype and Metabolic Health Traits . Genes.

Animesh Acharjee, Vartika Bisht, Katrina Nash, Yuanwei Xu, Prasoon Agarwal, Sofie Bosch, Georgios V. Gkoutos(2021). Integration of the Microbiome, Metabolome and Transcriptomics Data Identified Novel Metabolic Pathway Regulation in Colorectal Cancer . International Journal of Molecular Sciences. 22. (11). p. 5763. {MDPI} {AG}

Animesh Acharjee, Vartika Bisht, Katrina Nash, Yuanwei Xu, Prasoon Agarwal, Sofie Bosch, Georgios Gkoutos PhD DIC FRSB FRSM (2021). Integration of the Microbiome, Metabolome and Transcriptomics Data Identified Novel Metabolic Pathway Regulation in Colorectal Cancer . International Journal of Molecular Sciences.

Animesh Acharjee, Zsuzsanna Ament, Matthew B. Bevers, Zoe Wolcott, W. Taylor Kimberly(2021). Uric Acid and Gluconic Acid as Predictors of Hyperglycemia and Cytotoxic Injury after Stroke . Translational Stroke Research. 12. (2). p. 293--302. Springer Science and Business Media {LLC}

Traumatic injury is associated with reduced deoxyribonuclease activity and dysregulation of the actin scavenging system @article{2a2437dd6a3c47b8bcf9ad45d1c6a87c, title = "Traumatic injury is associated with reduced deoxyribonuclease activity and dysregulation of the actin scavenging system", abstract = "Background: Traumatic injury is associated with increased concentrations of cell-free DNA (cfDNA) in the circulation, which contribute to post-injury complications. The endonuclease deoxyribonuclease 1 (DNase-1) is responsible for removing 90% of circulating cfDNA. Recently, DNase activity was reported to be significantly reduced following major non-traumatic brain injury (TBI), but the processes responsible were not investigated. Moreover, it is not known how quickly following injury DNase activity is reduced and whether this also occurs after TBI.Methods: At 3 post-injury time points (≤1, 4–12 and 48–72 hours), blood samples were obtained from 155 adult trauma patients that had sustained an isolated TBI (n = 21), TBI with accompanying extracranial injury (TBI+) (n = 53) or an extracranial injury only (ECI) (n = 81). In addition to measuring cfDNA levels and the activity and expression of DNase, circulating concentrations of monomeric globular action (G-actin), an inhibitor of DNase-1, and the actin scavenging proteins gelsolin (GSN) and vitamin D binding protein (VDBP) were determined and values compared to a cohort of healthy controls.Results: Significantly elevated concentrations of plasma cfDNA were seen in TBI, TBI+ and ECI patients at all study time points when compared to healthy controls. cfDNA levels were significantly higher at ≤1 hour post-injury in ECI patients who subsequently developed multiple organ dysfunction syndrome when compared to those who did not. Plasma DNase-1 protein was significantly elevated in all patient groups at all sampling time points. In contrast, DNase enzyme activity was significantly reduced, with this impaired function evident in TBI+ patients within minutes of injury. Circulating concentrations of G-actin were elevated in all patient cohorts in the immediate aftermath of injury and this was accompanied by a significant reduction in the levels of GSN and VDBP.Conclusions: The post-traumatic increase in circulating cfDNA that occurs following extracranial trauma and TBI is accompanied by reduced DNase activity. We propose that, secondary to reduced GSN and VDBP levels, elevated circulating concentrations of G-actin underlie the post-injury reduction in DNase activity. Reducing circulating cfDNA levels via therapeutic restoration of DNase-1 activity may improve clinical outcomes post-injury.", keywords = "Cell-free DNA, Deoxyribonuclease, Extracellular actin scavenging system, Pre-hospital, Trauma", author = "Jon Hazeldine and Rob Dinsdale and David Naumann and Animesh Acharjee and Jon Bishop and Janet Lord and Paul Harrison", note = "Publisher Copyright: {\textcopyright} 2021 The Author(s) 2021. Published by Oxford University Press.", year = "2021", month = apr, day = "1", doi = "10.1093/burnst/tkab001", language = "English", volume = "9", journal = "Burns & Trauma", issn = "2321-3868", publisher = "Springer Verlag", } . Burns & Trauma.

(2021). Unique diagnostic signatures of concussion in the saliva of male athletes: the Study of Concussion in Rugby Union through MicroRNAs (SCRUM) . British journal of sports medicine.

Unique diagnostic signatures of concussion in the saliva of male athletes @article{798b3e9296834ca3b09af4781deb8f8e, title = "Unique diagnostic signatures of concussion in the saliva of male athletes: the Study of Concussion in Rugby Union through MicroRNAs (SCRUM)", abstract = "OBJECTIVE: To investigate the role of salivary small non-coding RNAs (sncRNAs) in the diagnosis of sport-related concussion.METHODS: Saliva was obtained from male professional players in the top two tiers of England's elite rugby union competition across two seasons (2017-2019). Samples were collected preseason from 1028 players, and during standardised head injury assessments (HIAs) at three time points (in-game, post-game, and 36-48 hours post-game) from 156 of these. Samples were also collected from controls (102 uninjured players and 66 players sustaining a musculoskeletal injury). Diagnostic sncRNAs were identified with next generation sequencing and validated using quantitative PCR in 702 samples. A predictive logistic regression model was built on 2017-2018 data (training dataset) and prospectively validated the following season (test dataset).RESULTS: The HIA process confirmed concussion in 106 players (HIA+) and excluded this in 50 (HIA-). 32 sncRNAs were significantly differentially expressed across these two groups, with let-7f-5p showing the highest area under the curve (AUC) at 36-48 hours. Additionally, a combined panel of 14 sncRNAs (let-7a-5p, miR-143-3p, miR-103a-3p, miR-34b-3p, RNU6-7, RNU6-45, Snora57, snoU13.120, tRNA18Arg-CCT, U6-168, U6-428, U6-1249, Uco22cjg1,YRNA_255) could differentiate concussed subjects from all other groups, including players who were HIA- and controls, immediately after the game (AUC 0.91, 95% CI 0.81 to 1) and 36-48 hours later (AUC 0.94, 95% CI 0.86 to 1). When prospectively tested, the panel confirmed high predictive accuracy (AUC 0.96, 95% CI 0.92 to 1 post-game and AUC 0.93, 95% CI 0.86 to 1 at 36-48 hours).CONCLUSIONS: SCRUM, a large prospective observational study of non-invasive concussion biomarkers, has identified unique signatures of concussion in saliva of male athletes diagnosed with concussion.", author = "{Di Pietro}, Valentina and Patrick O'Halloran and Watson, {Callum N} and Ghazala Begum and Animesh Acharjee and Yakoub, {Kamal M} and Conor Bentley and Davies, {David J} and Paolo Iliceto and Gabriella Candilera and Menon, {David K} and Cross, {Matthew J} and Stokes, {Keith A} and Kemp, {Simon Pt} and Antonio Belli", note = "{\textcopyright} Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.", year = "2021", month = mar, day = "23", doi = "10.1136/bjsports-2020-103274", language = "English", journal = "British Journal of Sports Medicine", issn = "0306-3674", publisher = "BMJ Publishing Group", } . British Journal of Sports Medicine.

(2021). Suppression of insulin-induced gene 1 (INSIG1) function promotes hepatic lipid remodelling and restrains NASH progression . Molecular metabolism.

Animesh Acharjee, Hassan Sadozai, Thomas Gruber, Beat Gloor, Eva Karamitopoulou(2021). Pancreatic Cancers with High Grade Tumor Budding Exhibit Hallmarks of Diminished Anti-Tumor Immunity . Cancers. 13. (5). p. 1090. {MDPI} {AG}

Animesh Acharjee, Hassan Sadozai, Thomas Gruber, Beat Gloor, Eva Karamitopoulou (2021). Pancreatic Cancers with High Grade Tumor Budding Exhibit Hallmarks of Diminished Anti-Tumor Immunity . Cancers.

Sadozai H, Acharjee A, Gruber T, Gloor B, Karamitopoulou E, Lin H(2021). Pancreatic Cancers with High Grade Tumor Budding Exhibit Hallmarks of Diminished Anti-Tumor Immunity . Cancers.

(2021). Distinct Stromal and Immune Features Collectively Contribute to Long-Term Survival in Pancreatic Cancer . Frontiers in immunology.

Breastfeeding promotes early neonatal regulatory T-cell expansion and immune tolerance of non-inherited maternal antigens @article{98fa6b7b19c742889d5c70091df25e38, title = "Breastfeeding promotes early neonatal regulatory T-cell expansion and immune tolerance of non-inherited maternal antigens", abstract = "Background: Breastfeeding is associated with long‐term health benefits, such as a lower incidence of childhood infections, asthma, obesity and autoimmune disorders. However, little is known regarding how the maternal and neonatal immune systems interact after parturition when the neonate receives nutrition from maternal breast milk.Methods: We undertook a comparative analysis of immune repertoire and function at birth and 3 weeks of age in a cohort of 38 term neonates born by caesarean section grouped according to feeding method (breast milk versus formula). We used flow cytometry to study the immune phenotype in neonatal and maternal blood samples and mixed lymphocyte reactions to establish the proliferation response of neonatal versus maternal lymphocytes and vice versa. The microbiome of neonatal stool samples was also investigated using 16S rRNA sequencing.Results: We show that the proportion of regulatory T cells (Tregs) increases in this period and is nearly twofold higher in exclusively breastfed neonates compared with those who received formula milk only. Moreover, breastfed neonates show a specific and Treg‐dependent reduction in proliferative T‐cell responses to non‐inherited maternal antigens (NIMA), associated with a reduction in inflammatory cytokine production. We also observed the enrichment of short chain fatty acid producing taxa (Veillonella and Gemella) in stool samples of exclusively breastfed neonates.Conclusions: These data indicate that exposure of the neonate to maternal cells through breastfeeding acts to drive the maturation of Tregs and {\textquoteleft}tolerizes{\textquoteright} the neonate towards NIMA.", keywords = "Th17, breastfeeding, microbiome, neonate, non-inherited maternal antigen, regulatory T cell", author = "Hannah Wood and Animesh Acharjee and Hayden Pearce and Quraishi, {Mohammed Nabil} and Richard Powell and Amanda Rossiter and Andrew Beggs and Andrew Ewer and Paul Moss and Gergely Toldi", note = "Funding Information: We are grateful for the guidance of our late colleague, mentor and friend, Shree Vishna Rasiah, and dedicate this work to his memory. We wish to thank Diane Mellers for coordinating the enrolment of study participants and the research midwives at Birmingham Women{\textquoteright}s Hospital for their support. We are also grateful for the technical support of Sam Nicol and Kriti Verma. A.A. was supported by the National Institute for Health Research (NIHR) Surgical Reconstruction and Microbiology Research Centre (SRMRC), Birmingham, UK. The views expressed in this publication are those of the authors and not necessarily those of the National Health Service or the National Institute for Health Research. Publisher Copyright: {\textcopyright} 2021 The Authors. Allergy published by European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.", year = "2021", month = jan, day = "12", doi = "10.1111/all.14736", language = "English", journal = "Allergy", issn = "0105-4538", publisher = "Wiley", } . Allergy.

Animesh Acharjee, Jon Hazeldine, Robert J Dinsdale, David N Naumann, Jonathan R B Bishop, Janet M Lord, Paul Harrison(2021). Traumatic injury is associated with reduced deoxyribonuclease activity and dysregulation of the actin scavenging system . Burns & Trauma. 9. Oxford University Press ({OUP})

Koch M, Acharjee A, Ament Z, Schleicher R, Bevers M, Stapleton C, Patel A, Kimberly WT(2021). Machine Learning-Driven Metabolomic Evaluation of Cerebrospinal Fluid: Insights Into Poor Outcomes After Aneurysmal Subarachnoid Hemorrhage . Neurosurgery.

Animesh Acharjee, Saraswati Koppad, Annappa B, Georgios V Gkoutos(2021). Cloud Computing Enabled Big Multi-Omics Data Analytics . Bioinformatics and Biology Insights. 15. p. 117793222110359. {SAGE} Publications

Animesh Acharjee, Joseph Larkman, Yuanwei Xu, Victor Roth Cardoso, Georgios V. Gkoutos(2020). A random forest based biomarker discovery and power analysis framework for diagnostics research . BMC Medical Genomics. 13. (1). Springer Science and Business Media {LLC}

A random forest based biomarker discovery and power analysis framework for diagnostics research @article{1b9b958c51804e10a44a4010e38da2d6, title = "A random forest based biomarker discovery and power analysis framework for diagnostics research", abstract = "BACKGROUND: Biomarker identification is one of the major and important goal of functional genomics and translational medicine studies. Large scale -omics data are increasingly being accumulated and can provide vital means for the identification of biomarkers for the early diagnosis of complex disease and/or for advanced patient/diseases stratification. These tasks are clearly interlinked, and it is essential that an unbiased and stable methodology is applied in order to address them. Although, recently, many, primarily machine learning based, biomarker identification approaches have been developed, the exploration of potential associations between biomarker identification and the design of future experiments remains a challenge.METHODS: In this study, using both simulated and published experimentally derived datasets, we assessed the performance of several state-of-the-art Random Forest (RF) based decision approaches, namely the Boruta method, the permutation based feature selection without correction method, the permutation based feature selection with correction method, and the backward elimination based feature selection method. Moreover, we conducted a power analysis to estimate the number of samples required for potential future studies.RESULTS: We present a number of different RF based stable feature selection methods and compare their performances using simulated, as well as published, experimentally derived, datasets. Across all of the scenarios considered, we found the Boruta method to be the most stable methodology, whilst the Permutation (Raw) approach offered the largest number of relevant features, when allowed to stabilise over a number of iterations. Finally, we developed and made available a web interface ( https://joelarkman.shinyapps.io/PowerTools/ ) to streamline power calculations thereby aiding the design of potential future studies within a translational medicine context.CONCLUSIONS: We developed a RF-based biomarker discovery framework and provide a web interface for our framework, termed PowerTools, that caters the design of appropriate and cost-effective subsequent future omics study.", keywords = "Biomarker, Feature selection, Power study, Random forest", author = "Animesh Acharjee and Joseph Larkman and Yuanwei Xu and Cardoso, {Victor Roth} and Gkoutos, {Georgios V}", year = "2020", month = nov, day = "23", doi = "10.1186/s12920-020-00826-6", language = "English", volume = "13", journal = "BMC Medical Genomics", issn = "1755-8794", publisher = "BioMed Central", number = "1", } . BMC Medical Genomics.

Animesh Acharjee, Furqan Aziz, John A. Williams, Dominic Russ, Laura Bravo-Merodio, Georgios V. Gkoutos(2020). Biomarker Prioritisation and Power Estimation Using Ensemble Gene Regulatory Network Inference . International Journal of Molecular Sciences. 21. (21). p. 7886. {MDPI} {AG}

Biomarker Prioritisation and Power Estimation Using Ensemble Gene Regulatory Network Inference @article{d8208be7bd7940028849f7ceeb1307de, title = "Biomarker Prioritisation and Power Estimation Using Ensemble Gene Regulatory Network Inference", abstract = "Inferring the topology of a gene regulatory network (GRN) from gene expression data is a challenging but important undertaking for gaining a better understanding of gene regulation. Key challenges include working with noisy data and dealing with a higher number of genes than samples. Although a number of different methods have been proposed to infer the structure of a GRN, there are large discrepancies among the different inference algorithms they adopt, rendering their meaningful comparison challenging. In this study, we used two methods, namely the MIDER (Mutual Information Distance and Entropy Reduction) and the PLSNET (Partial least square based feature selection) methods, to infer the structure of a GRN directly from data and computationally validated our results. Both methods were applied to different gene expression datasets resulting from inflammatory bowel disease (IBD), pancreatic ductal adenocarcinoma (PDAC), and acute myeloid leukaemia (AML) studies. For each case, gene regulators were successfully identified. For example, for the case of the IBD dataset, the UGT1A family genes were identified as key regulators while upon analysing the PDAC dataset, the SULF1 and THBS2 genes were depicted. We further demonstrate that an ensemble-based approach, that combines the output of the MIDER and PLSNET algorithms, can infer the structure of a GRN from data with higher accuracy. We have also estimated the number of the samples required for potential future validation studies. Here, we presented our proposed analysis framework that caters not only to candidate regulator genes prediction for potential validation experiments but also an estimation of the number of samples required for these experiments.", keywords = "Causal modelling, Experimental design, Gene regulatory network, Omics integration", author = "Furqan Aziz and Animesh Acharjee and John Williams and Dominic Russ and Laura Bravo-Merodio and Georgios Gkoutos", year = "2020", month = oct, day = "23", doi = "10.3390/ijms21217886", language = "English", volume = "21", pages = "1--22", journal = "International Journal of Molecular Sciences", issn = "1661-6596", publisher = "MDPI", number = "21", } . International Journal of Molecular Sciences.

Animesh Acharjee, Furqan Aziz, John A. Williams, Dominic Russ, Laura Bravo-Merodio, Georgios V. Gkoutos (2020). Biomarker Prioritisation and Power Estimation Using Ensemble Gene Regulatory Network Inference . International Journal of Molecular Sciences.

Liu KD, Acharjee A, Hinz C, Liggi S, Murgia A, Denes J, Gulston MK, Wang X, Chu Y, West JA, et al.(2020). Consequences of Lipid Remodeling of Adipocyte Membranes Being Functionally Distinct from Lipid Storage in Obesity . Journal of proteome research.

(2020). The Consequences of Lipid Remodelling of Adipocyte Membranes Being Functionally Distinct From Lipid Storage in Obesity. Journal of Proteome Research.

A pilot integrative analysis of colonic gene expression, gut microbiota, and immune infiltration in primary sclerosing cholangitis-inflammatory bowel disease @article{e4d2f44eb3604b9a9e9f8c117c5864c5, title = "A pilot integrative analysis of colonic gene expression, gut microbiota, and immune infiltration in primary sclerosing cholangitis-inflammatory bowel disease: association of disease with bile acid pathways", abstract = "BACKGROUND: Although a majority of patients with PSC have colitis [PSC-IBD; primary sclerosing cholangitis-inflammatory bowel disease], this is phenotypically different from ulcerative colitis [UC]. We sought to define further the pathophysiological differences between PSC-IBD and UC, by applying a comparative and integrative approach to colonic gene expression, gut microbiota and immune infiltration data. METHODS: Colonic biopsies were collected from patients with PSC-IBD [n = 10], UC [n = 10], and healthy controls [HC; n = 10]. Shotgun RNA-sequencing for differentially expressed colonic mucosal genes [DEGs], 16S rRNA analysis for microbial profiling, and immunophenotyping were performed followed by multi-omic integration. RESULTS: The colonic transcriptome differed significantly between groups [p = 0.01]. Colonic transcriptomes from HC were different from both UC [1343 DEGs] and PSC-IBD [4312 DEGs]. Of these genes, only 939 had shared differential gene expression in both UC and PSC-IBD compared with HC. Imputed pathways were predominantly associated with upregulation of immune response and microbial defense in both disease cohorts compared with HC. There were 1692 DEGs between PSC-IBD and UC. Bile acid signalling pathways were upregulated in PSC-IBD compared with UC [p = 0.02]. Microbiota profiles were different between the three groups [p = 0.01]; with inferred function in PSC-IBD also being consistent with dysregulation of bile acid metabolism. Th17 cells and IL17-producing CD4 cells were increased in both PSC-IBD and UC when compared with HC [p < 0.05]. Multi-omic integration revealed networks involved in bile acid homeostasis and cancer regulation in PSC-IBD. CONCLUSIONS: Colonic transcriptomic and microbiota analysis in PSC-IBD point toward dysregulation of colonic bile acid homeostasis compared with UC. This highlights important mechanisms and suggests the possibility of novel approaches in treating PSC-IBD.", keywords = "Autoimmune liver disease, bioinformatics, colitis, dysbiosis", author = "Quraishi, {Mohammed Nabil} and Animesh Acharjee and Beggs, {Andrew D} and Richard Horniblow and Chris Tselepis and Georgios Gkoutos and Subrata Ghosh and Amanda Rossiter and Nicholas Loman and {van Schaik}, Willem and David Withers and Walters, {Julian R F} and Hirschfield, {Gideon M} and Iqbal, {Tariq H}", year = "2020", month = jul, doi = "10.1093/ecco-jcc/jjaa021", language = "English", volume = "14", pages = "935--947", journal = "Journal of Crohn's & Colitis", issn = "1873-9946", publisher = "Oxford University Press", number = "7", } . Journal of Crohn's & Colitis.

(2020). Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz) . Plant methods.

Animesh Acharjee, Veronica Lolli, Donato Angelino, Michele Tassotti, Daniele Del Rio, Pedro Mena, Augusta Caligiani (2020). Chemical Characterization of Capsule-Brewed Espresso Coffee Aroma from the Most Widespread Italian Brands by HS-SPME/GC-MS . Molecules.

Animesh Acharjee, Veronica Lolli, Donato Angelino, Michele Tassotti, Daniele Del Rio, Pedro Mena, Augusta Caligiani (2020). Chemical Characterization of Capsule-Brewed Espresso Coffee Aroma from the Most Widespread Italian Brands by HS-SPME/GC-MS . Molecules.

Quraishi MN, Acharjee A, Beggs AD, Horniblow R, Tselepis C, Gkoutus G, Ghosh S, Rossiter A, Loman N, van Schaik W, et al.(2020). A pilot integrative analysis of colonic gene expression, gut microbiota and immune infiltration in primary sclerosing cholangitis-inflammatory bowel disease: association of disease with bile acid pathways . Journal of Crohn's & colitis.

(2020). OP09 Immunomodulatory mechanisms of faecal microbiota transplantation are associated with clinical response in ulcerative colitis: early results from STOP-Colitis . Journal of Crohn's and Colitis.

(2020). P297 Novel serum soluble markers of histological healing in ulcerative colitis . Journal of Crohn's and Colitis.

(2020). P005 Regulators of the guanylate cyclase pathway are potential novel markers of mucosal healing in IBD . Journal of Crohn's and Colitis.

Machine learning for the detection of early immunological markers as predictors of multi-organ dysfunction @article{2e88efd2f02b47ca9368441fb2439154, title = "Machine learning for the detection of early immunological markers as predictors of multi-organ dysfunction", abstract = "The immune response to major trauma has been analysed mainly within post-hospital admission settings where the inflammatory response is already underway and the early drivers of clinical outcome cannot be readily determined. Thus, there is a need to better understand the immediate immune response to injury and how this might influence important patient outcomes such as multi-organ dysfunction syndrome (MODS). In this study, we have assessed the immune response to trauma in 61 patients at three different post-injury time points (ultra-early (<=1h), 4-12h, 48-72h) and analysed relationships with the development of MODS. We developed a pipeline using Absolute Shrinkage and Selection Operator and Elastic Net feature selection methods that were able to identify 3 physiological features (decrease in neutrophil CD62L and CD63 expression and monocyte CD63 expression and frequency) as possible biomarkers for MODS development. After univariate and multivariate analysis for each feature alongside a stability analysis, the addition of these 3 markers to standard clinical trauma injury severity scores yields a Generalized Liner Model (GLM) with an average Area Under the Curve value of 0.92±0.06. This performance provides an 8% improvement over the Probability of Survival (PS14) outcome measure and a 13% improvement over the New Injury Severity Score (NISS) for identifying patients at risk of MODS.", author = "Laura Bravo and Animesh Acharjee and Jon Hazeldine and Conor Bentley and Mark Foster and Georgios Gkoutos and Janet Lord", year = "2019", month = dec, day = "19", doi = "10.1038/s41597-019-0337-6", language = "English", volume = "6", pages = "1--10", journal = "Scientific Data", issn = "2052-4463", publisher = "Nature Publishing Group", } . Scientific Data.

Animesh Acharjee, Laura Bravo-Merodio, Jon Hazeldine, Conor Bentley, Mark Foster, Georgios V. Gkoutos, Janet M. Lord (2019). Machine learning for the detection of early immunological markers as predictors of multi-organ dysfunction . Scientific Data.

Animesh Acharjee, Laura Bravo-Merodio, John A. Williams, Georgios V. Gkoutos(2019). -Omics biomarker identification pipeline for translational medicine . Journal of Translational Medicine. 17. (1). Springer Science and Business Media {LLC}

High-throughput metabolite profiling @article{bc9b829c15444299bc4d8fc1a4e8e1af, title = "High-throughput metabolite profiling: identification of plasma taurine as a potential biomarker of functional outcome after aneurysmal subarachnoid hemorrhage", abstract = "OBJECTIVEMetabolite profiling (or metabolomics) can identify candidate biomarkers for disease and potentially uncover new pathways for intervention. The goal of this study was to identify potential biomarkers of functional outcome after subarachnoid hemorrhage (SAH).METHODSThe authors performed high-throughput metabolite profiling across a broad spectrum of chemical classes (163 metabolites) on plasma samples taken from 191 patients with SAH who presented to Massachusetts General Hospital between May 2011 and October 2016. Samples were drawn at 3 time points following ictus: 0–5, 6–10, and 11–14 days. Elastic net (EN) and LASSO (least absolute shrinkage and selection operator) machine learning analyses were performed to identify metabolites associated with 90-day functional outcomes as assessed by the modified Rankin Scale (mRS). Additional univariate and multivariate analyses were then conducted to further examine the relationship between metabolites and clinical variables and 90-day functional outcomes.RESULTSOne hundred thirty-seven (71.7%) patients with aneurysmal SAH met the criteria for inclusion. A good functional outcome (mRS score 0–2) at 90 days was found in 79 (57.7%) patients. Patients with good outcomes were younger (p = 0.002), had lower admission Hunt and Hess grades (p < 0.0001) and modified Fisher grades (p < 0.0001), and did not develop hydrocephalus (p < 0.0001) or delayed cerebral ischemia (DCI) (p = 0.049). EN and LASSO machine learning methods identified taurine as the leading metabolite associated with 90-day functional outcome (p < 0.0001). Plasma concentrations of the amino acid taurine from samples collected between days 0 and 5 after aneurysmal SAH were 21.9% (p = 0.002) higher in patients with good versus poor outcomes. Logistic regression demonstrated that taurine remained a significant predictor of functional outcome (p = 0.013; OR 3.41, 95% CI 1.28–11.4), after adjusting for age, Hunt and Hess grade, modified Fisher grade, hydrocephalus, and DCI.CONCLUSIONSElevated plasma taurine levels following aneurysmal SAH predict a good 90-day functional outcome. While experimental evidence in animals suggests that this effect may be mediated through downregulation of pro-inflammatory cytokines, additional studies are required to validate this hypothesis in humans.", keywords = "aneurysm, biomarker, machine learning, metabolomics, subarachnoid hemorrhage, vascular disorders", author = "Stapleton, {Christopher J.} and Animesh Acharjee and Irvine, {Hannah J.} and Wolcott, {Zoe C.} and Patel, {Aman B.} and Kimberly, {W. Taylor}", year = "2019", month = nov, day = "22", doi = "10.3171/2019.9.JNS191346", language = "English", pages = "1--8", journal = "Journal of Neurosurgery", issn = "0022-3085", publisher = "American Association of Neurological Surgeons", } . Journal of Neurosurgery.

Stapleton CJ, Acharjee A, Irvine HJ, Wolcott ZC, Patel AB, Kimberly WT(2019). High-throughput metabolite profiling: identification of plasma taurine as a potential biomarker of functional outcome after aneurysmal subarachnoid hemorrhage . Journal of neurosurgery.

(2019). 125 - Altered DNA Methylation and Serum Levels of Metabolites is Associated With Type 2 Diabetes in Adolescents . Canadian Journal of Diabetes.

Animesh Acharjee, Pedro Mena, Iziar A. Ludwig, Virginia B. Tomatis, Luca Calani, Alice Rosi, Furio Brighenti, Sumantra Ray, Julian L. Griffin, Les J. Bluck, et al.(2019). Inter-individual variability in the production of flavan-3-ol colonic metabolites: preliminary elucidation of urinary metabotypes . European Journal of Nutrition. 58. (4). p. 1529--1543. Springer Science and Business Media {LLC}

Inter-individual variability in the production of flavan-3-ol colonic metabolites @article{3cb963f34413403e91bbd14d0ce15fec, title = "Inter-individual variability in the production of flavan-3-ol colonic metabolites: preliminary elucidation of urinary metabotypes", abstract = "PurposeThere is much information on the bioavailability of (poly)phenolic compounds following acute intake of various foods. However, there are only limited data on the effects of repeated and combined exposure to specific (poly)phenol food sources and the inter-individual variability in their bioavailability. This study evaluated the combined urinary excretion of (poly)phenols from green tea and coffee following daily consumption by healthy subjects in free-living conditions. The inter-individual variability in the production of phenolic metabolites was also investigated.MethodsEleven participants consumed both tablets of green tea and green coffee bean extracts daily for 8 weeks and 24-h urine was collected on five different occasions. The urinary profile of phenolic metabolites and a set of multivariate statistical tests were used to investigate the putative existence of characteristic metabotypes in the production of flavan-3-ol microbial metabolites.Results(Poly)phenolic compounds in the green tea and green coffee bean extracts were absorbed and excreted after simultaneous consumption, with green tea resulting in more inter-individual variability in urinary excretion of phenolic metabolites. Three metabotypes in the production of flavan-3-ol microbial metabolites were tentatively defined, characterized by the excretion of different amounts of trihydroxyphenyl-γ-valerolactones, dihydroxyphenyl-γ-valerolactones, and hydroxyphenylpropionic acids.ConclusionsThe selective production of microbiota-derived metabolites from flavan-3-ols and the putative existence of characteristic metabotypes in their production represent an important development in the study of the bioavailability of plant bioactives. These observations will contribute to better understand the health effects and individual differences associated with consumption of flavan-3-ols, arguably the main class of flavonoids in the human diet.", keywords = "polyphenols, green tea catechins, coffee caffeoylquinic acids, colonic microbiota, urinary phenotype, metabotypes", author = "Pedro Mena and Ludwig, {Iziar A.} and Tomatis, {Virginia B.} and Animesh Acharjee and Luca Calani and Alice Rosi and Furio Brighenti and Sumantra Ray and Griffin, {Julian L.} and Bluck, {Les J.} and {Del Rio}, {Daniele Del Rio}", year = "2019", month = jun, doi = "10.1007/s00394-018-1683-4", language = "English", volume = "58", pages = "1529--1543", journal = "European Journal of Nutrition", issn = "1436-6207", publisher = "D. Steinkopff-Verlag", number = "4", } . European Journal of Nutrition.

-Omics biomarker identification pipeline for translational medicine @article{2c70f97cda184bd486c49231c82ca73a, title = "-Omics biomarker identification pipeline for translational medicine", abstract = "BACKGROUND: Translational medicine (TM) is an emerging domain that aims to facilitate medical or biological advances efficiently from the scientist to the clinician. Central to the TM vision is to narrow the gap between basic science and applied science in terms of time, cost and early diagnosis of the disease state. Biomarker identification is one of the main challenges within TM. The identification of disease biomarkers from -omics data will not only help the stratification of diverse patient cohorts but will also provide early diagnostic information which could improve patient management and potentially prevent adverse outcomes. However, biomarker identification needs to be robust and reproducible. Hence a robust unbiased computational framework that can help clinicians identify those biomarkers is necessary.METHODS: We developed a pipeline (workflow) that includes two different supervised classification techniques based on regularization methods to identify biomarkers from -omics or other high dimension clinical datasets. The pipeline includes several important steps such as quality control and stability of selected biomarkers. The process takes input files (outcome and independent variables or -omics data) and pre-processes (normalization, missing values) them. After a random division of samples into training and test sets, Least Absolute Shrinkage and Selection Operator and Elastic Net feature selection methods are applied to identify the most important features representing potential biomarker candidates. The penalization parameters are optimised using 10-fold cross validation and the process undergoes 100 iterations and a combinatorial analysis to select the best performing multivariate model. An empirical unbiased assessment of their quality as biomarkers for clinical use is performed through a Receiver Operating Characteristic curve and its Area Under the Curve analysis on both permuted and real data for 1000 different randomized training and test sets. We validated this pipeline against previously published biomarkers.RESULTS: We applied this pipeline to three different datasets with previously published biomarkers: lipidomics data by Acharjee et al. (Metabolomics 13:25, 2017) and transcriptomics data by Rajamani and Bhasin (Genome Med 8:38, 2016) and Mills et al. (Blood 114:1063-1072, 2009). Our results demonstrate that our method was able to identify both previously published biomarkers as well as new variables that add value to the published results.CONCLUSIONS: We developed a robust pipeline to identify clinically relevant biomarkers that can be applied to different -omics datasets. Such identification reveals potentially novel drug targets and can be used as a part of a machine-learning based patient stratification framework in the translational medicine settings.", keywords = "Biomarker, -Omics, Regularization, Feature selection, Translational medicine", author = "Laura Bravo-Merodio and Williams, {John A.} and Gkoutos, {Georgios V.} and Animesh Acharjee", year = "2019", month = may, day = "14", doi = "10.1186/s12967-019-1912-5", language = "English", volume = "17", journal = "Journal of translational medicine", issn = "1479-5876", publisher = "Springer", number = "1", } . Journal of translational medicine.

Animesh Acharjee, Hannah J Irvine, Zoe Wolcott, Zsuzsanna Ament, Holly Hinson, Bradley J Molyneaux, J Marc Simard, Kevin N Sheth, W Taylor Kimberly(2019). Abstract TMP18: Intravenous Glibenclamide Reduces Hypoxanthine, a Novel Predictor of Brain Edema Formation: An Exploratory Analysis of GAMES-RP . Stroke. 50. (Suppl{\_}1). Ovid Technologies (Wolters Kluwer Health)

Animesh Acharjee, Jonathan P. Segal, Benjamin H. Mullish, Mohammed Nabil Quraishi, Horace R. T. Williams, Tariq Iqbal, Ailsa L. Hart, Julian R. Marchesi(2019). The application of omics techniques to understand the role of the gut microbiota in inflammatory bowel disease . Therapeutic Advances in Gastroenterology. 12. p. 175628481882225. {SAGE} Publications

Hepatic steatosis risk is partly driven by increased de novo lipogenesis following carbohydrate consumption @article{6c27c051b9b748899e8d8f460487758c, title = "Hepatic steatosis risk is partly driven by increased de novo lipogenesis following carbohydrate consumption", abstract = "BackgroundDiet is a major contributor to metabolic disease risk, but there is controversy as to whether increased incidences of diseases such as non-alcoholic fatty liver disease arise from consumption of saturated fats or free sugars. Here, we investigate whether a sub-set of triacylglycerols (TAGs) were associated with hepatic steatosis and whether they arise from de novo lipogenesis (DNL) from the consumption of carbohydrates.ResultsWe conduct direct infusion mass spectrometry of lipids in plasma to study the association between specific TAGs and hepatic steatosis assessed by ultrasound and fatty liver index in volunteers from the UK-based Fenland Study and evaluate clustering of TAGs in the National Survey of Health and Development UK cohort. We find that TAGs containing saturated and monounsaturated fatty acids with 16–18 carbons are specifically associated with hepatic steatosis. These TAGs are additionally associated with higher consumption of carbohydrate and saturated fat, hepatic steatosis, and variations in the gene for protein phosphatase 1, regulatory subunit 3b (PPP1R3B), which in part regulates glycogen synthesis. DNL is measured in hyperphagic ob/ob mice, mice on a western diet (high in fat and free sugar) and in healthy humans using stable isotope techniques following high carbohydrate meals, demonstrating the rate of DNL correlates with increased synthesis of this cluster of TAGs. Furthermore, these TAGs are increased in plasma from patients with biopsy-confirmed steatosis.ConclusionA subset of TAGs is associated with hepatic steatosis, even when correcting for common confounding factors. We suggest that hepatic steatosis risk in western populations is in part driven by increased DNL following carbohydrate rich meals in addition to the consumption of saturated fat.", keywords = "non-alcoholic fatty liver disease, direct infusion mass spectrometry, de novo lipogenesis, triacylglycerols, triglycerides", author = "Sanders, {Francis W B} and Animesh Acharjee and Celia Walker and Luke Marney and Roberts, {Lee D} and Fumiaki Imamura and Benjamin Jenkins and Jack Case and Sumantra Ray and Samuel Virtue and Antonio Vidal-Puig and Diana Kuh and Rebecca Hardy and Michael Allison and Nita Forouhi and Murray, {Andrew J} and Nick Wareham and Michele Vacca and Albert Koulman and Griffin, {Julian L}", year = "2018", month = jun, day = "20", doi = "10.1186/s13059-018-1439-8", language = "English", volume = "19", pages = "1--15", journal = "Genome Biology", issn = "1474-7596", publisher = "BioMed Central", } . Genome Biology.

Animesh Acharjee, Francis W. B. Sanders, Celia Walker, Luke Marney, Lee D. Roberts, Fumiaki Imamura, Benjamin Jenkins, Jack Case, Sumantra Ray, Samuel Virtue, et al.(2018). Hepatic steatosis risk is partly driven by increased de novo lipogenesis following carbohydrate consumption . Genome Biology. 19. (1). Springer Nature

Metabolomics and lipidomics study of mouse models of type 1 diabetes highlights divergent metabolism in purine and tryptophan metabolism prior to disease onset @article{a91bd533fbdf4d6993e58fc290e89bc0, title = "Metabolomics and lipidomics study of mouse models of type 1 diabetes highlights divergent metabolism in purine and tryptophan metabolism prior to disease onset", abstract = "With the increase in incidence of type 1 diabetes (T1DM), there is an urgent need to understand the early molecular and metabolic alterations that accompany the autoimmune disease. This is not least because in murine models early intervention can prevent the development of disease. We have applied a liquid chromatography (LC−) and gas chromatography (GC−) mass spectrometry (MS) metabolomics and lipidomics analysis of blood plasma and pancreas tissue to follow the progression of disease in three models related to autoimmune diabetes: the nonobese diabetic (NOD) mouse, susceptible to the development of autoimmune diabetes, and the NOD-E (transgenic NOD mice that express the I-E heterodimer of the major histocompatibility complex II) and NOD-severe combined immunodeficiency (SCID) mouse strains, two models protected from the development of diabetes. All three analyses highlighted the metabolic differences between the NOD-SCID mouse and the other two strains, regardless of diabetic status indicating that NOD-SCID mice are poor controls for metabolic changes in NOD mice. By comparing NOD and NOD-E mice, we show the development of T1DM in NOD mice is associated with changes in lipid, purine, and tryptophan metabolism, including an increase in kynurenic acid and a decrease in lysophospholipids, metabolites previously associated with inflammation.", keywords = "nonobese diabetic (NOD) mouse, NOD-severe combined immunodeficiency (SCID) mouse, mass spectrometry, xanthinine, kynurenic acid", author = "Murfitt, {Steven A.} and Paola Zaccone and Xinzhu Wang and Animesh Acharjee and Yvonne Sawyer and Albert Koulman and Roberts, {Lee D} and Anne Cooke and Griffin, {Julian Leether}", year = "2018", month = mar, day = "2", doi = "https://doi.org/10.1021/acs.jproteome.7b00489", language = "English", volume = "17", pages = "946−960", journal = "Journal of Proteome Research", issn = "1535-3893", publisher = "American Chemical Society", number = "3", } . Journal of Proteome Research.

Animesh Acharjee, Steven A. Murfitt, Paola Zaccone, Xinzhu Wang, Yvonne Sawyer, Albert Koulman, Lee D. Roberts, Anne Cooke, Julian Leether Griffin(2018). Metabolomics and Lipidomics Study of Mouse Models of Type 1 Diabetes Highlights Divergent Metabolism in Purine and Tryptophan Metabolism Prior to Disease Onset . Journal of Proteome Research. 17. (3). p. 946--960. American Chemical Society ({ACS})

Genetical genomics of quality related traits in potato tubers using proteomics @article{5496734c6e184ebaa29abd1de76574a3, title = "Genetical genomics of quality related traits in potato tubers using proteomics", abstract = "BackgroundRecent advances in ~omics technologies such as transcriptomics, metabolomics and proteomics along with genotypic profiling have permitted the genetic dissection of complex traits such as quality traits in non-model species. To get more insight into the genetic factors underlying variation in quality traits related to carbohydrate and starch metabolism and cold sweetening, we determined the protein content and composition in potato tubers using 2D–gel electrophoresis in a diploid potato mapping population. Upon analyzing we made sure that the proteins from the patatin family were excluded to ensure a better representation of the other proteins.ResultsWe subsequently performed pQTL analyses for all other proteins with a sufficient representation in the population and established a relationship between proteins and 26 potato tuber quality traits (e.g. flesh colour, enzymatic discoloration) by co-localization on the genetic map and a direct correlation study of protein abundances and phenotypic traits. Over 1643 unique protein spots were detected in total over the two harvests. We were able to map pQTLs for over 300 different protein spots some of which co-localized with traits such as starch content and cold sweetening. pQTLs were observed on every chromosome although not evenly distributed over the chromosomes. The largest number of pQTLs was found for chromosome 8 and the lowest for chromosome number 10. For some 20 protein spots multiple QTLs were observed.ConclusionsFrom this analysis, hotspot areas for protein QTLs were identified on chromosomes three, five, eight and nine. The hotspot on chromosome 3 coincided with a QTL previously identified for total protein content and had more than 23 pQTLs in the region from 70 to 80 cM. Some of the co-localizing protein spots associated with some of the most interesting tuber quality traits were identified, albeit far less than we had anticipated at the onset of the experiments.", keywords = "genetical genomics, proteomics, protein QTL, potato quality traits", author = "Animesh Acharjee and Pierre-Yves Chibon and Bjorn Kloosterman and Twan America and Jenny Renaut and Chris Maliepaard and Visser, {Richard G. F.}", year = "2018", month = jan, day = "23", doi = "https://doi.org/10.1186/s12870-018-1229-1", language = "English", volume = "18", pages = "1--10", journal = "BMC Plant Biology", issn = "1471-2229", publisher = "Springer", } . BMC Plant Biology.

Animesh Acharjee, Pierre-Yves Chibon, Bjorn Kloosterman, Twan America, Jenny Renaut, Chris Maliepaard, Richard G. F. Visser(2018). Genetical genomics of quality related traits in potato tubers using proteomics . BMC Plant Biology. 18. (1). Springer Nature

Animesh Acharjee, Biplab Bhattacharjee, Muhammad Shafi(2017). Network mining based elucidation of the dynamics of cross-market clustering and connectedness in Asian region: An MST and hierarchical clustering approach . Journal of King Saud University - Computer and Information Sciences. Elsevier {BV}

Animesh Acharjee, Philippa Prentice, Carlo Acerini, James Smith, Ieuan A. Hughes, Ken Ong, Julian L. Griffin, David Dunger, Albert Koulman(2017). The translation of lipid profiles to nutritional biomarkers in the study of infant metabolism . Metabolomics. 13. (3). Springer Nature

Animesh Acharjee, Biplab Bhattacharjee, Muhammad Shafi(2017). Investigating the Evolution of Linkage Dynamics among Equity Markets Using Network Models and Measures: The Case of Asian Equity Market Integration . Data. 2. (4). p. 41. {MDPI} {AG}

Animesh Acharjee, Zsuzsanna Ament, James A. West, Elizabeth Stanley, Julian L. Griffin(2016). Integration of metabolomics, lipidomics and clinical data using a machine learning method . BMC Bioinformatics. 17. (S15). Springer Nature

Animesh Acharjee, Biplab Bhattacharjee, Muhammad Shafi, Wei-Xing Zhou(2016). Investigating the Influence Relationship Models for Stocks in Indian Equity Market: A Weighted Network Modelling Study . PLOS ONE. 11. (11). p. e0166087. Public Library of Science ({PLoS})

Animesh Acharjee, Bjorn Kloosterman, Richard G. F. Visser, Chris Maliepaard(2016). Integration of multi-omics data for prediction of phenotypic traits using random forest . BMC Bioinformatics. 17. (S5). Springer Nature

Animesh Acharjee(2012). Comparison of Regularized Regression Methods for ~Omics Data . Journal of Postgenomics Drug & Biomarker Development. 03. (03). {OMICS} Publishing Group

Carreno-Quintero, N., Acharjee, A., Maliepaard, C., Bachem, C.W.B., Mumm, R., Bouwmeester, H., Visser, R.G.F., Keurentjes, J.J.B.(2012). Untargeted metabolic quantitative trait loci analyses reveal a relationship between primary metabolism and potato tuber quality . Plant Physiology. 158. (3). p. 1306-1318.

Animesh Acharjee, Bjorn Kloosterman, Ric C.H. de Vos, Jeroen S. Werij, Christian W.B. Bachem, Richard G.F. Visser, Chris Maliepaard(2011). Data integration and network reconstruction with ∼omics data using Random Forest regression in potato . Analytica Chimica Acta. 705. (1-2). p. 56--63. Elsevier {BV}

(2020). Translational biomarkers in the era of precision medicine . Advances in Clinical Chemistry.

Zardilis, A., Dias, J., Acharjee, A., Smith, J.(2014). Extensible and executable stochastic models of fatty acid and lipid metabolism . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8859. p. 244-247.