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Abstract Precision medicine relies on identifying reliable biomarkers for gene dependencies to tailor individualized therapeutic strategies. The advent of high-throughput technologies presents unprecedented opportunities to explore molecular disease mechanisms but also challenges due to high dimensionality and collinearity among features. Traditional statistical methods often fall short in this context, necessitating novel computational approaches that harness the full potential of big data in bioinformatics. Here, we introduce a novel machine learning approach extending the Least Absolute Shrinkage and Selection Operator (LASSO) regression framework to incorporate biological knowledge, such as protein-protein interaction databases, into the regularization process. This bio-primed approach prioritizes variables that are both statistically significant and biologically relevant. Applying our method to multiple dependency datasets, we identified biomarkers which traditional methods overlooked. Our biologically informed LASSO method effectively identifies relevant biomarkers from high-dimensional collinear data, bridging the gap between statistical rigor and biological insight. This method holds promise for advancing personalized medicine by uncovering novel therapeutic targets and understanding the complex interplay of genetic and molecular factors in disease.

Abstract Background Synthetic human milk oligosaccharides (HMOs) are used to supplement infant formula despite limited understanding of their impact on the post-weaned developing gut microbiota. Here, we assess the influence of 0.5 g/L 2-fucosyllactose (2’FL) and 4.0 g/L pooled HMOs (pHMOs) on the composition and activity of cultured fecal-derived microbial communities from seven healthy young children. Results Exposure to pHMOs induced significant shifts in both the microbial community composition and metabolic output, including an increased abundance of several genera, notably Bacteroides, and the production of health-associated metabolites. In contrast, 2’FL alone did not lead to substantial changes in the communities. A total of 330 bacterial isolates, spanning 157 species, were cultured from these communities and individually evaluated for their responses to HMOs. Over 100 non-Bifidobacterium species showed enhanced growth upon pHMOs treatment and a high degree of intraspecies variation in HMO metabolism was observed. Conclusion Our study provides valuable insight into the health-enhancing properties of HMOs while highlighting the need for future research into the efficacy of incorporating individual structures into infant formula, particularly when aiming to modulate the gut microbiota. Video Abstract

Human milk oligosaccharides (HMOs) are a set of complex carbohydrates and the third largest solid component of human milk, after lactose and lipids. To date, over 150 HMOs have been identified and the diversity of structures produced by lactating women is influenced by maternal genetics as well as other maternal, infant, and environmental factors. While the concentrations of individual HMOs have been shown to vary between individuals and throughout the course of lactation, the variability of HMO concentration profiles following different pregnancies occurring in the same woman is presently unknown. As such, the objective of this study was to compare HMO concentrations in human milk samples provided by the same women (n = 34) following repeat pregnancies. We leveraged existing human milk samples and metadata from the UC San Diego Human Milk Research Biorepository (HMB) and measured the concentrations of the 19 most abundant HMOs using high-performance liquid chromatography with fluorescence detection (HPLC-FL). By assessing dissimilarities in HMO concentration profiles, as well as concentration trends in individual structures between pregnancies of each participant, we discovered that HMO profiles largely follow a highly personalized and predictable trajectory following different pregnancies irrespective of non-genetic influences. In conclusion, this is the first study to assess the interactions between parity and time following delivery on variations in HMO compositions.

Enzymes are nature's catalysts, mediating chemical processes in living systems. The study of enzyme function and mechanism includes defining the maximum catalytic rate and affinity for substrate/s (among other factors), referred to as enzyme kinetics. Enzyme kinetics is a staple of biochemistry curricula and other disciplines, from molecular and cellular biology to pharmacology. However, because enzyme kinetics involves concepts rarely employed in other areas of biology, it can be challenging for students and researchers. Traditional graphical analysis was replaced by computational analysis, requiring another skill not core to many life sciences curricula. Computational analysis can be time-consuming and difficult in free software (e.g., R) or require costly software (e.g., GraphPad Prism). We present Enzyme Kinetics Analysis (EKA), a web-tool to augment teaching and learning and streamline EKA. EKA is an interactive and free tool for analyzing enzyme kinetic data and improving student learning through simulation, built using R and RStudio's ShinyApps. EKA provides kinetic models (Michaelis-Menten, Hill, simple reversible inhibition models, ternary-complex, and ping-pong) for users to fit experimental data, providing graphical results and statistics. Additionally, EKA enables users to input parameters and create data and graphs, to visualize changes to parameters (e.g., K[subscript M] or number of measurements). This function is designed for students learning kinetics but also for researchers to design experiments. EKA (enzyme-kinetics.shinyapps.io/enzkinet_webpage/) provides a simple, interactive interface for teachers, students, and researchers to explore enzyme kinetics. It gives researchers the ability to design experiments and analyze data without specific software requirements.

N -Acetylneuraminic acid (Neu5Ac) is a negatively charged nine-carbon amino sugar that is often the peripheral sugar in human cell-surface glycoconjugates. Some bacteria scavenge, import, and metabolize Neu5Ac or redeploy it on their cell surfaces for immune evasion. The import of Neu5Ac by many bacteria is mediated by tripartite ATP-independent periplasmic (TRAP) transporters. We have previously reported the structures of SiaQM, a membrane-embedded component of the Haemophilus influenzae TRAP transport system, (Currie et al., 2024). However, none of the published structures contain Neu5Ac bound to SiaQM. This information is critical for defining the transport mechanism and for further structure-activity relationship studies. Here, we report the structures of Fusobacterium nucleatum SiaQM with and without Neu5Ac. Both structures are in an inward (cytoplasmic side) facing conformation. The Neu5Ac-bound structure reveals the interactions of Neu5Ac with the transporter and its relationship with the Na+ binding sites. Two of the Na+-binding sites are similar to those described previously. We identify a third metal-binding site that is further away and buried in the elevator domain. Ser300 and Ser345 interact with the C1-carboxylate group of Neu5Ac. Proteoliposome-based transport assays showed that Ser300-Neu5Ac interaction is critical for transport, whereas Ser345 is dispensable. Neu5Ac primarily interacts with residues in the elevator domain of the protein, thereby supporting the elevator with an operator mechanism. The residues interacting with Neu5Ac are conserved, providing fundamental information required to design inhibitors against this class of proteins.

This study quantifies the influences of anthropogenic forcing to date on precipitation over Aotearoa New Zealand (ANZ). Large ensembles of simulations from the weather@home regional climate model experiments are analysed under two scenarios, a natural (NAT) or counter-factual scenario which excludes human-induced changes to the climate system and an anthropogenic (ANT) or factual scenario. The impacts of anthropogenic forcing on precipitation are analysed in the context of large-scale circulation types characterized using an existing Self Organizing Map classification. The combined effect of both thermodynamics and dynamics are compared with values expected from the Clausius–Clapeyron (C–C) relation. Changes in the precipitation intensity due to greenhouse gas-forced temperature rise are lower than the expected C–C value. However extreme precipitation changes approach the C–C value for some circulation types. Specifically westerly flows enhance precipitation change across ANZ relative to the C–C rate, particularly over the West Coast. Conversely, northwesterly flows reduce the change over the North Island relative to the C–C value. Moreover, the wet day frequency generally reduces in the ANT scenario relative to NAT, reductions are largest on the West Coast of the South Island for westerly flows. Additionally, the frequency of days with extreme precipitation rises over ANZ for most circulation patterns, except in Northland and for northwesterly flows. This underscores the combined influence of dynamics and thermodynamics in shaping both precipitation intensity and frequency patterns across ANZ.

Ortho-phthalates (herein referred to as phthalates) are synthetic chemicals used in thousands of different everyday products and materials. Nearly ubiquitous environmental exposure is reflected by phthalate metabolites in the urine of almost all Canadians. However, phthalate exposure tends to be higher amongst people of low socioeconomic status and ethnic minorities. Substantial evidence shows that certain phthalates cause harm to human health, particularly developing fetuses and children. Governments vary in their approach to assessing and managing risks associated with phthalates. Canada continues to take a more permissive stance on phthalate regulations compared to the EU and some US states. We argue that the recent Canadian national risk assessment on phthalates does not appropriately reflect the growing evidence demonstrating harm to human health from phthalate exposure and does not adequately consider the evidence showing higher exposures faced by vulnerable populations. Canadians would benefit from adopting a more stringent regulatory approach to phthalates. Specifically, Canada should expand phthalate restrictions to apply to all consumer products, implement sunset dates toward eliminating the use of existing phthalates, and mandate publicly available evidence of no harm for phthalate alternatives. Canadian alignment on phthalate regulations with the EU and a growing number of US states could encourage other countries to follow suit.

The pharmaceutical industry is increasingly embracing laboratory automation to enhance experimental efficiency and operational resilience, particularly through the integration of automated liquid handlers (ALHs). This paper explores the integration of the low-cost Opentrons OT-2 liquid handling robot with F. Hoffmann-La Roche AG's in-house workflow orchestration software, AutoLab, to overcome barriers to lab automation. By leveraging the OT-2′s development-oriented interfaces and AutoLab's modular architecture, we achieved a user-friendly, cost-efficient, and flexible automation solution that aligns with FAIR (findable, accessible, interoperable, reusable) data principles. We demonstrate an advanced workflow development methodology, utilizing the software architecture, that facilitates the creation of two flexible pipetting protocols and medium complexity assays. This deep integration approach diminishes the learning curve for novice users while simultaneously enhancing the overall efficiency and reliability of the experimental workflow. Our findings suggest that such integrations can significantly mitigate the challenges associated with lab automation, including cost, complexity, and adaptability, paving the way for more accessible and robust automated systems in pharmaceutical research.