Computational Analysis of the Gut Microbiota-Mediated Drug Metabolism.

The gut microbiota, an extensive ecosystem harboring trillions of bacteria, plays a pivotal role in human health and disease, influencing diverse conditions from obesity to cancer. Among the microbiota's myriad functions, the capacity to metabolize drugs remains relatively unexplored despite its potential implications for drug efficacy and toxicity. Experimental methods are resource-intensive, prompting the need for innovative computational approaches.

JESTR: Joint Embedding Space Technique for Ranking Candidate Molecules for the Annotation of Untargeted Metabolomics Data.

Motivation: A major challenge in metabolomics is annotation: assigning molecular structures to mass spectral fragmentation patterns. Despite recent advances in molecule-to-spectra and in spectra-to-molecular fingerprint prediction (FP), annotation rates remain low.

Results: We introduce in this paper a novel paradigm (JESTR) for annotation.

Manipulation of Lipid Nanocapsules as an Efficient Intranasal Platform for Brain Deposition of Clozapine as an Antipsychotic Drug.

Background/objectives: The blood-brain barrier (BBB) significantly limits the treatment of central nervous system disorders, such as schizophrenia, by restricting drug delivery to the brain. This study explores the potential of intranasal clozapine-loaded lipid nanocapsules (IN LNCs) as a targeted and effective delivery system to the brain.

Methods: LNCs were prepared using the phase inversion technique and characterized in terms of size, zeta potential, entrapment efficiency (EE%), and in vitro drug release.

MassSpecGym: A benchmark for the discovery and identification of molecules.

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts.

An Ensemble Spectral Prediction (ESP) model for metabolite annotation.

Motivation: A key challenge in metabolomics is annotating measured spectra from a biological sample with chemical identities. Currently, only a small fraction of measurements can be assigned identities. Two complementary computational approaches have emerged to address the annotation problem: mapping candidate molecules to spectra, and mapping query spectra to molecular candidates.

Extending PROXIMAL to predict degradation pathways of phenolic compounds in the human gut microbiota.

Despite significant advances in reconstructing genome-scale metabolic networks, the understanding of cellular metabolism remains incomplete for many organisms. A promising approach for elucidating cellular metabolism is analysing the full scope of enzyme promiscuity, which exploits the capacity of enzymes to bind to non-annotated substrates and generate novel reactions. To guide time-consuming costly experimentation, different computational methods have been proposed for exploring enzyme promiscuity.

NAIF: A novel artificial intelligence-based tool for accurate diagnosis of stage F3/F4 liver fibrosis in the general adult population, validated with three external datasets.

Objective: The purpose of this study was to determine the effectiveness of a new AI-based tool called NAIF (NAFLD-AI-Fibrosis) in identifying individuals from the general population with advanced liver fibrosis (stage F3/F4). We compared NAIF's performance to two existing risk score calculators, aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis-4 (Fib4).

Methods: To set up the algorithm for diagnosing severe liver fibrosis (defined as Fibroscan® values E ≥ 9.

Molecular structure discovery for untargeted metabolomics using biotransformation rules and global molecular networking.

Although untargeted mass spectrometry-based metabolomics is crucial for understanding life's molecular underpinnings, its effectiveness is hampered by low annotation rates of the generated tandem mass spectra. To address this issue, we introduce a novel data-driven approach, Biotransformation-based Annotation Method (BAM), that leverages molecular structural similarities inherent in biochemical reactions. BAM operates by applying biotransformation rules to known 'anchor' molecules, which exhibit high spectral similarity to unknown spectra, thereby hypothesizing and ranking potential structures for the corresponding 'suspect' molecule.

Pharmacological Management of Cholera: A Century of Expert Opinions in Cecil Textbook of Medicine.

Background: Cholera is a potentially lethal diarrheal disease produced by Vibrio cholerae serotypes O1 El Tor and O139. Known since antiquity, the condition causes epidemics in many areas, particularly in Asia, Africa, and South America. Left untreated, the mortality may reach 50%.

CSI: Contrastive data Stratification for Interaction prediction and its application to compound-protein interaction prediction.

Motivation: Accurately predicting the likelihood of interaction between two objects (compound-protein sequence, user-item, author-paper, etc.) is a fundamental problem in Computer Science. Current deep-learning models rely on learning accurate representations of the interacting objects.

Differences in protein expression, at the basal state and at 2 h of insulin infusion, in muscle biopsies from healthy Arab men with high or low insulin sensitivity measured by hyperinsulinemic euglycemic clamp.

Background: Skeletal muscle is the main site for insulin-dependent glucose disposal. The hyperinsulinemic euglycemic clamp (HIEC) is the gold standard for the assessment of insulin sensitivity (IS). We have previously shown that insulin sensitivity, measured by HIEC, varied widely among a group of 60 young healthy men with normoglycemia.

Using graph neural networks for site-of-metabolism prediction and its applications to ranking promiscuous enzymatic products.

Motivation: While traditionally utilized for identifying site-specific metabolic activity within a compound to alter its interaction with a metabolizing enzyme, predicting the site-of-metabolism (SOM) is essential in analyzing the promiscuity of enzymes on substrates. The successful prediction of SOMs and the relevant promiscuous products has a wide range of applications that include creating extended metabolic models (EMMs) that account for enzyme promiscuity and the construction of novel heterologous synthesis pathways. There is therefore a need to develop generalized methods that can predict molecular SOMs for a wide range of metabolizing enzymes.

Setting up of a machine learning algorithm for the identification of severe liver fibrosis profile in the general US population cohort.

Background: The progress of digital transformation in clinical practice opens the door to transforming the current clinical line for liver disease diagnosis from a late-stage diagnosis approach to an early-stage based one. Early diagnosis of liver fibrosis can prevent the progression of the disease and decrease liver-related morbidity and mortality. We developed here a machine learning (ML) algorithm containing standard parameters that can identify liver fibrosis in the general US population.

Improving the annual monitoring rates of testosterone replacement therapy patients in primary care.

Introduction: Testosterone replacement therapy (TRT) is the treatment of choice for male hypogonadism. British Society for Sexual Medicine (BSSM) guidelines on adult testosterone deficiency recommend that TRT patients undergo annual monitoring of their testosterone levels and potential complications of treatment; though evidence suggests that substantial numbers of men on TRT are not monitored adequately.

Methods: Review of the electronic patient record from a single general practice in southwest Scotland revealed that only 1 of 26 (4%) TRT patients had been monitored as per BSSM guidelines in the previous 12 months.

Boost-RS: boosted embeddings for recommender systems and its application to enzyme-substrate interaction prediction.

Motivation: Despite experimental and curation efforts, the extent of enzyme promiscuity on substrates continues to be largely unexplored and under documented. Providing computational tools for the exploration of the enzyme-substrate interaction space can expedite experimentation and benefit applications such as constructing synthesis pathways for novel biomolecules, identifying products of metabolism on ingested compounds, and elucidating xenobiotic metabolism. Recommender systems (RS), which are currently unexplored for the enzyme-substrate interaction prediction problem, can be utilized to provide enzyme recommendations for substrates, and vice versa.

Insulin sensitivity variations in apparently healthy Arab male subjects: correlation with insulin and C peptide.

Introduction: Decreased insulin sensitivity occurs early in type 2 diabetes (T2D). T2D is highly prevalent in the Middle East and North Africa regions. This study assessed the variations in insulin sensitivity in normal apparently healthy subjects and the levels of adiponectin, adipsin and inflammatory markers.

Artificial Intelligence for Biology.

Despite efforts to integrate research across different subdisciplines of biology, the scale of integration remains limited. We hypothesize that future generations of Artificial Intelligence (AI) technologies specifically adapted for biological sciences will help enable the reintegration of biology. AI technologies will allow us not only to collect, connect, and analyze data at unprecedented scales, but also to build comprehensive predictive models that span various subdisciplines.

Analysis of metabolic network disruption in engineered microbial hosts due to enzyme promiscuity.

Increasing understanding of metabolic and regulatory networks underlying microbial physiology has enabled creation of progressively more complex synthetic biological systems for biochemical, biomedical, agricultural, and environmental applications. However, despite best efforts, confounding phenotypes still emerge from unforeseen interplay between biological parts, and the design of robust and modular biological systems remains elusive. Such interactions are difficult to predict when designing synthetic systems and may manifest during experimental testing as inefficiencies that need to be overcome.

Enzyme Promiscuity Prediction Using Hierarchy-Informed Multi-Label Classification.

Motivation: As experimental efforts are costly and time consuming, computational characterization of enzyme capabilities is an attractive alternative. We present and evaluate several machine-learning models to predict which of 983 distinct enzymes, as defined via the Enzyme Commission (EC) numbers, are likely to interact with a given query molecule. Our data consists of enzyme-substrate interactions from the BRENDA database.

Learning graph representations of biochemical networks and its application to enzymatic link prediction.

Motivation: The complete characterization of enzymatic activities between molecules remains incomplete, hindering biological engineering and limiting biological discovery. We develop in this work a technique, enzymatic link prediction (ELP), for predicting the likelihood of an enzymatic transformation between two molecules. ELP models enzymatic reactions cataloged in the KEGG database as a graph.

The metabolic footprint of compromised insulin sensitivity under fasting and hyperinsulinemic-euglycemic clamp conditions in an Arab population.

Metabolic pathways that are corrupted at early stages of insulin resistance (IR) remain elusive. This study investigates changes in body metabolism in clinically healthy and otherwise asymptomatic subjects that may become apparent already under compromised insulin sensitivity (IS) and prior to IR. 47 clinically healthy Arab male subjects with a broad range of IS, determined by hyperinsulinemic-euglycemic clamp (HIEC), were investigated.

A method for the generation of human stem cell-derived alpha cells.

The generation of pancreatic cell types from renewable cell sources holds promise for cell replacement therapies for diabetes. Although most effort has focused on generating pancreatic beta cells, considerable evidence indicates that glucagon secreting alpha cells are critically involved in disease progression and proper glucose control. Here we report on the generation of stem cell-derived human pancreatic alpha (SC-alpha) cells from pluripotent stem cells via a transient pre-alpha cell intermediate.

Pathway-Activity Likelihood Analysis and Metabolite Annotation for Untargeted Metabolomics Using Probabilistic Modeling.

: Untargeted metabolomics comprehensively characterizes small molecules and elucidates activities of biochemical pathways within a biological sample. Despite computational advances, interpreting collected measurements and determining their biological role remains a challenge. : To interpret measurements, we present an inference-based approach, termed Probabilistic modeling for Untargeted Metabolomics Analysis (PUMA).

ASAP-SML: An antibody sequence analysis pipeline using statistical testing and machine learning.

Antibodies are capable of potently and specifically binding individual antigens and, in some cases, disrupting their functions. The key challenge in generating antibody-based inhibitors is the lack of fundamental information relating sequences of antibodies to their unique properties as inhibitors. We develop a pipeline, Antibody Sequence Analysis Pipeline using Statistical testing and Machine Learning (ASAP-SML), to identify features that distinguish one set of antibody sequences from antibody sequences in a reference set.