Institute of Chartered Accountants of Nigeria

About: Institute of Chartered Accountants of Nigeria is a based out in . It is known for research contribution in the topics: Population & Adipose tissue. The organization has 528 authors who have published 579 publications receiving 18688 citations.

Mutation affecting the conserved acidic WNK1 motif causes inherited hyperkalemic hyperchloremic acidosis

Abstract: Gain-of-function mutations in with no lysine (K) 1 (WNK1) and WNK4 genes are responsible for familial hyperkalemic hypertension (FHHt), a rare, inherited disorder characterized by arterial hypertension and hyperkalemia with metabolic acidosis. More recently, FHHt-causing mutations in the Kelch-like 3-Cullin 3 (KLHL3-CUL3) E3 ubiquitin ligase complex have shed light on the importance of WNK's cellular degradation on renal ion transport. Using full exome sequencing for a 4-generation family and then targeted sequencing in other suspected cases, we have identified new missense variants in the WNK1 gene clustering in the short conserved acidic motif known to interact with the KLHL3-CUL3 ubiquitin complex. Affected subjects had an early onset of a hyperkalemic hyperchloremic phenotype, but normal blood pressure values"Functional experiments in Xenopus laevis oocytes and HEK293T cells demonstrated that these mutations strongly decrease the ubiquitination of the kidney-specific isoform KS-WNK1 by the KLHL3-CUL3 complex rather than the long ubiquitous catalytically active L-WNK1 isoform. A corresponding CRISPR/Cas9 engineered mouse model recapitulated both the clinical and biological phenotypes. Renal investigations showed increased activation of the Ste20 proline alanine-rich kinase-Na+-Cl- cotransporter (SPAK-NCC) phosphorylation cascade, associated with impaired ROMK apical expression in the distal part of the renal tubule. Together, these new WNK1 genetic variants highlight the importance of the KS-WNK1 isoform abundance on potassium homeostasis.

Interpretable and Accurate Prediction Models for Metagenomics Data

Abstract: BACKGROUND: Microbiome biomarker discovery for patient diagnosis, prognosis, and risk evaluation is attracting broad interest. Selected groups of microbial features provide signatures that characterize host disease states such as cancer or cardio-metabolic diseases. Yet, the current predictive models stemming from machine learning still behave as black boxes and seldom generalize well. Their interpretation is challenging for physicians and biologists, which makes them difficult to trust and use routinely in the physician-patient decision-making process. Novel methods that provide interpretability and biological insight are needed. Here, we introduce "predomics", an original machine learning approach inspired by microbial ecosystem interactions that is tailored for metagenomics data. It discovers accurate predictive signatures and provides unprecedented interpretability. The decision provided by the predictive model is based on a simple, yet powerful score computed by adding, subtracting, or dividing cumulative abundance of microbiome measurements. RESULTS: Tested on >100 datasets, we demonstrate that predomics models are simple and highly interpretable. Even with such simplicity, they are at least as accurate as state-of-the-art methods. The family of best models, discovered during the learning process, offers the ability to distil biological information and to decipher the predictability signatures of the studied condition. In a proof-of-concept experiment, we successfully predicted body corpulence and metabolic improvement after bariatric surgery using pre-surgery microbiome data. CONCLUSIONS: Predomics is a new algorithm that helps in providing reliable and trustworthy diagnostic decisions in the microbiome field. Predomics is in accord with societal and legal requirements that plead for an explainable artificial intelligence approach in the medical field.

The contribution of microbial biotechnology to economic growth and employment creation

Abstract: Our communication discusses the profound impact of bio-based economies – in particular microbial biotechnologies – on SDG 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all. A bio-based economy provides significant potential for improving labour supply, education and investment, and thereby for substantially increasing the demographic dividend. This, in turn, improves the sustainable development of economies.

SREBP-1c and lipogenesis in the liver: an update1.

Abstract: Sterol Regulatory Element Binding Protein-1c is a transcription factor that controls the synthesis of lipids from glucose in the liver, a process which is of utmost importance for the storage of energy. Discovered in the early nineties by B. Spiegelman and by M. Brown and J. Goldstein, it has generated more than 5000 studies in order to elucidate its mechanism of activation and its role in physiology and pathology. Synthetized as a precursor found in the membranes of the endoplasmic reticulum, it has to be exported to the Golgi and cleaved by a mechanism called regulated intramembrane proteolysis. We reviewed in 2002 its main characteristics, its activation process and its role in the regulation of hepatic glycolytic and lipogenic genes. We particularly emphasized that Sterol Regulatory Element Binding Protein-1c is the mediator of insulin effects on these genes. In the present review, we would like to update these informations and focus on the response to insulin and to another actor in Sterol Regulatory Element Binding Protein-1c activation, the endoplasmic reticulum stress.

Deep learning identifies morphological features in breast cancer predictive of cancer ERBB2 status and trastuzumab treatment efficacy

Abstract: The treatment of patients with ERBB2 (HER2)-positive breast cancer with anti-ERBB2 therapy is based on the detection of ERBB2 gene amplification or protein overexpression. Machine learning (ML) algorithms can predict the amplification of ERBB2 based on tumor morphological features, but it is not known whether ML-derived features can predict survival and efficacy of anti-ERBB2 treatment. In this study, we trained a deep learning model with digital images of hematoxylin-eosin (HE 95% CI, 0.15-0.93; P = 0.034). A high H&E-ERBB2 score was associated with unfavorable survival in patients with ERBB2-negative cancer as determined by CISH. ERBB2-associated morphology correlated with the efficacy of adjuvant anti-ERBB2 treatment and can contribute to treatment-predictive information in breast cancer.