The human gut microbiota is central in regulating all facets of host physiology, and in early life it is thought to influence the host’s immune system and metabolism, affecting long-term health. However, longitudinally monitored cohorts with parallel analysis of faecal samples and health data are scarce. In our observational study we describe the gut microbiota development in the first 2 years of life and create a gut microbiota wellbeing index based on the microbiota development and health data in a cohort of nearly 1000 infants using clustering and trajectory modelling. We show that infants’ gut microbiota development is highly predictable, following one of five trajectories, dependent on infant exposures, and predictive of later health outcomes. We characterise the natural healthy gut microbiota trajectory and several different dysbiotic trajectories associated with different health outcomes. Bifidobacterium and Bacteroides appear as early keystone organisms, directing microbiota development and consistently predicting positive health outcomes. A microbiota wellbeing index, based on the healthy development trajectory, is predictive of general health over the first 5 years. The results indicate that gut microbiota succession is part of infant physiological development, predictable, and malleable. This information can be utilised to improve the predictions of individual health risks.

Although the infant gut microbiota has been extensively studied, comprehensive assessment on the microbiota determinants including technical variables has not been performed in large infant cohorts. We studied the effect of 109 variables on the 16S rRNA gene amplicon-based gut microbiota profiles of infants sampled longitudinally from three weeks to two years of life in the Finnish HELMi birth cohort. Spot faecal samples from both parents were included for intra-family analyses, totalling to 7657 samples from 985 families that were evaluated for beta-diversity patterns using permutational multivariate analysis on Bray–Curtis distances, and differential abundance testing and alpha-diversity for variables of interest. We also assessed the effect of different taxonomic levels and distance methods. In time point-specific models, the largest share of variation explained, up to 2–6%, were seen in decreasing order for the DNA extraction batch, delivery mode and related perinatal exposures, defecation frequency and parity/siblings. Variables describing the infant gastrointestinal function were continuously important during the first two years, reflecting changes in e.g., feeding habits. The effect of parity/siblings on infant microbiota was modified by birth mode and exposure to intrapartum antibiotics, exemplifying the tight interlinkage of perinatal factors relevant for infant microbiota research. In total, up to 19% of the biological microbiota variation in the infant gut could be explained. Our results highlight the need to interpret variance partitioning results in the context of each cohort's characteristics and microbiota processing. Our study provides a comprehensive report of key factors associated with infant gut microbiota composition across the two first years of life in a homogenous cohort. The study highlights possible important future research areas and confounding factors to be considered. This research was supported by 10.13039/501100014438 Business Finland , 10.13039/501100002341 Academy of Finland , Foundation for Nutrition Research and the Doctoral Program in Microbiology and Biotechnology, University of Helsinki, Finland.

Significance: Disturbance of glutathione (GSH) metabolism is a hallmark of numerous diseases, yet GSH functions are poorly understood. One key to this question is to consider its functional compartmentation. GSH is present in the endoplasmic reticulum (ER), where it competes with substrates for oxidation by the oxidative folding machinery, composed in eukaryotes of the thiol oxidase Ero1 and proteins from the disulfide isomerase family (protein disulfide isomerase). Yet, whether GSH is required for proper ER oxidative protein folding is a highly debated question. Recent Advances: Oxidative protein folding has been thoroughly dissected over the past decades, and its actors and their mode of action elucidated. Genetically encoded GSH probes have recently provided an access to subcellular redox metabolism, including the ER. Critical Issues: Of the few often-contradictory models of the role of GSH in the ER, the most popular suggest it serves as reducing power. Yet, as a reductant, GSH also activates Ero1, which questions how GSH can nevertheless support protein reduction. Hence, whether GSH operates in the ER as a reductant, an oxidant, or just as a “blank” compound mirroring ER/periplasm redox activity is a highly debated question, which is further stimulated by the puzzling occurrence of GSH in the Escherichia coli periplasmic “secretory” compartment, aside from the Dsb thiol-reducing and oxidase pathways. Future Directions: Addressing the mechanisms controlling GSH traffic in and out of the ER/periplasm and its recycling will help address GSH function in secretion. In addition, as thioredoxin reductase was recently implicated in ER oxidative protein folding, the relative contribution of each of these two reducing pathways should now be addressed. Antioxid. Redox Signal. 00, 000–000.

Shotgun metagenomics provides powerful insights into microbial community biodiversity and function. Yet, inferences from metagenomic studies are often limited by dataset size and complexity and are restricted by the availability and completeness of existing databases. De novo comparative metagenomics enables the comparison of metagenomes based on their total genetic content. We developed a tool called Libra that performs an all-vs-all comparison of metagenomes for precise clustering based on their k-mer content. Libra uses a scalable Hadoop framework for massive metagenome comparisons, Cosine Similarity for calculating the distance using sequence composition and abundance while normalizing for sequencing depth, and a web-based implementation in iMicrobe (http://imicrobe.us) that uses the CyVerse advanced cyberinfrastructure to promote broad use of the tool by the scientific community. A comparison of Libra to equivalent tools using both simulated and real metagenomic datasets, ranging from 80 million to 4.2 billion reads, reveals that methods commonly implemented to reduce compute time for large datasets, such as data reduction, read count normalization, and presence/absence distance metrics, greatly diminish the resolution of large-scale comparative analyses. In contrast, Libra uses all of the reads to calculate k-mer abundance in a Hadoop architecture that can scale to any size dataset to enable global-scale analyses and link microbial signatures to biological processes.

Tools allowing for the identification of viral sequences in host-associated and environmental metagenomes allows for a better understanding of the genetics and ecology of viruses and their hosts. Recently, new approaches using machine learning methods to distinguish viral from bacterial signal using k-mer sequence signatures were published for identifying viral contigs in metagenomes. The promise of these content-based approaches is the ability to discover new viruses, with no or few known relatives. In this perspective paper, we examine the use of the content-based machine learning tool VirFinder for the identification of viral sequences in aquatic metagenomes and explore the possibility of using ecosystem-focused models targeted to marine metagenomes. We discuss the impact of the training set composition on the tool performance and the current limitation for the retrieval of low abundance viral sequences in metagenomes. We identify potential biases that could arise from machine learning approaches for viral hunting in real-world datasets and suggest possible avenues to overcome them.