The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health.

Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities, as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods, and describe new scenarios to be unlocked in the next few years in the context of current knowledge

Methodological Considerations in Longitudinal Analyses of Microbiome Data: A Comprehensive Review

Biological processes underlying health and disease are inherently dynamic and are best understood when characterized in a time-informed manner. In this comprehensive review, we discuss challenges inherent in time-series microbiome data analyses and compare available approaches and methods to overcome them. Appropriate handling of longitudinal microbiome data can shed light on important roles, functions, patterns, and potential interactions between large numbers of microbial taxa or genes in the context of health, disease, or interventions. We present a comprehensive review and comparison of existing microbiome time-series analysis methods, for both preprocessing and downstream analyses, including differential analysis, clustering, network inference, and trait classification. We posit that the careful selection and appropriate utilization of computational tools for longitudinal microbiome analyses can help advance our understanding of the dynamic host–microbiome relationships that underlie health-maintaining homeostases, progressions to disease-promoting dysbioses, as well as phases of physiologic development like those encountered in childhood

The Swiss Primary Hypersomnolence and Narcolepsy Cohort study (SPHYNCS): Study protocol for a prospective, multicentre cohort observational study

Narcolepsy type 1 (NT1) is a disorder with well-established markers and a suspected autoimmune aetiology. Conversely, the narcoleptic borderland (NBL) disorders, including narcolepsy type 2, idiopathic hypersomnia, insufficient sleep syndrome and hypersomnia associated with a psychiatric disorder, lack well-defined markers and remain controversial in terms of aetiology, diagnosis and management. The Swiss Primary Hypersomnolence and Narcolepsy Cohort Study (SPHYNCS) is a comprehensive multicentre cohort study, which will investigate the clinical picture, pathophysiology and long-term course of NT1 and the NBL. The primary aim is to validate new and reappraise well-known markers for the characterization of the NBL, facilitating the diagnostic process. Seven Swiss sleep centres, belonging to the Swiss Narcolepsy Network (SNaNe), joined the study and will prospectively enrol over 500 patients with recent onset of excessive daytime sleepiness (EDS), hypersomnia or a suspected central disorder of hypersomnolence (CDH) during a 3-year recruitment phase. Healthy controls and patients with EDS due to severe sleep-disordered breathing, improving after therapy, will represent two control groups of over 50 patients each. Clinical and electrophysiological (polysomnography, multiple sleep latency test, maintenance of wakefulness test) information, and information on psychomotor vigilance and a sustained attention to response task, actigraphy and wearable devices (long-term monitoring), and responses to questionnaires will be collected at baseline and after 6, 12, 24 and 36 months. Potential disease markers will be searched for in blood, cerebrospinal fluid and stool. Analyses will include quantitative hypocretin measurements, proteomics/peptidomics, and immunological, genetic and microbiota studies. SPHYNCS will increase our understanding of CDH and the relationship between NT1 and the NBL. The identification of new disease markers is expected to lead to better and earlier diagnosis, better prognosis and personalized management of CDH

The Swiss Primary Hypersomnolence and Narcolepsy Cohort study (SPHYNCS): Study protocol for a prospective, multicentre cohort observational study.

Narcolepsy type 1 (NT1) is a disorder with well-established markers and a suspected autoimmune aetiology. Conversely, the narcoleptic borderland (NBL) disorders, including narcolepsy type 2, idiopathic hypersomnia, insufficient sleep syndrome and hypersomnia associated with a psychiatric disorder, lack well-defined markers and remain controversial in terms of aetiology, diagnosis and management. The Swiss Primary Hypersomnolence and Narcolepsy Cohort Study (SPHYNCS) is a comprehensive multicentre cohort study, which will investigate the clinical picture, pathophysiology and long-term course of NT1 and the NBL. The primary aim is to validate new and reappraise well-known markers for the characterization of the NBL, facilitating the diagnostic process. Seven Swiss sleep centres, belonging to the Swiss Narcolepsy Network (SNaNe), joined the study and will prospectively enrol over 500 patients with recent onset of excessive daytime sleepiness (EDS), hypersomnia or a suspected central disorder of hypersomnolence (CDH) during a 3-year recruitment phase. Healthy controls and patients with EDS due to severe sleep-disordered breathing, improving after therapy, will represent two control groups of over 50 patients each. Clinical and electrophysiological (polysomnography, multiple sleep latency test, maintenance of wakefulness test) information, and information on psychomotor vigilance and a sustained attention to response task, actigraphy and wearable devices (long-term monitoring), and responses to questionnaires will be collected at baseline and after 6, 12, 24 and 36 months. Potential disease markers will be searched for in blood, cerebrospinal fluid and stool. Analyses will include quantitative hypocretin measurements, proteomics/peptidomics, and immunological, genetic and microbiota studies. SPHYNCS will increase our understanding of CDH and the relationship between NT1 and the NBL. The identification of new disease markers is expected to lead to better and earlier diagnosis, better prognosis and personalized management of CDH

The Swiss Primary Hypersomnolence and Narcolepsy Cohort study (SPHYNCS): Study protocol for a prospective, multicentre cohort observational study

Narcolepsy type 1 (NT1) is a disorder with well-established markers and a suspected autoimmune aetiology. Conversely, the narcoleptic borderland (NBL) disorders, including narcolepsy type 2, idiopathic hypersomnia, insufficient sleep syndrome and hypersomnia associated with a psychiatric disorder, lack well-defined markers and remain controversial in terms of aetiology, diagnosis and management. The Swiss Primary Hypersomnolence and Narcolepsy Cohort Study (SPHYNCS) is a comprehensive multicentre cohort study, which will investigate the clinical picture, pathophysiology and long-term course of NT1 and the NBL. The primary aim is to validate new and reappraise well-known markers for the characterization of the NBL, facilitating the diagnostic process. Seven Swiss sleep centres, belonging to the Swiss Narcolepsy Network (SNaNe), joined the study and will prospectively enrol over 500 patients with recent onset of excessive daytime sleepiness (EDS), hypersomnia or a suspected central disorder of hypersomnolence (CDH) during a 3-year recruitment phase. Healthy controls and patients with EDS due to severe sleep-disordered breathing, improving after therapy, will represent two control groups of over 50 patients each. Clinical and electrophysiological (polysomnography, multiple sleep latency test, maintenance of wakefulness test) information, and information on psychomotor vigilance and a sustained attention to response task, actigraphy and wearable devices (long-term monitoring), and responses to questionnaires will be collected at baseline and after 6, 12, 24 and 36 months. Potential disease markers will be searched for in blood, cerebrospinal fluid and stool. Analyses will include quantitative hypocretin measurements, proteomics/peptidomics, and immunological, genetic and microbiota studies. SPHYNCS will increase our understanding of CDH and the relationship between NT1 and the NBL. The identification of new disease markers is expected to lead to better and earlier diagnosis, better prognosis and personalized management of CDH

Revealing General Patterns of Microbiomes That Transcend Systems: Potential and Challenges of Deep Transfer Learning

Special Series: Deciphering the Microbio.A growing body of research has established that the microbiome can mediate the dynamics and functional capacities of diverse biological systems. Yet, we understand little about what governs the response of these microbial communities to host or environmental changes. Most efforts to model microbiomes focus on defining the relationships between the microbiome, host, and environmental features within a specified study system and therefore fail to capture those that may be evident across multiple systems. In parallel with these developments in microbiome research, computer scientists have developed a variety of machine learning tools that can identify subtle, but informative, patterns from complex data. Here, we recommend using deep transfer learning to resolve microbiome patterns that transcend study systems. By leveraging diverse public data sets in an unsupervised way, such models can learn contextual relationships between features and build on those patterns to perform subsequent tasks (e.g., classification) within specific biological contexts.We thank the National Science Foundation for the funding of this work under grant number URoL:MTM2 2025457.Peer reviewe

Maternal prenatal stress, infant microbiota, brain, and behavioral development : The FinnBrain Birth Cohort Study

The gut microbiota and its manipulation have been shown to affect behavior and neurodevelopment in rodents. Likewise, maternal prenatal stress is known to influence offspring health and development as well as gut microbiota composition in rodents and non-human primates. However, how infant fecal microbiota is associated with infant behavioral and brain developmental phenotypes or with exposure to prenatal stress remains largely unknown. The first aim of this dissertation was to explore how infant fecal microbiota associates with temperament, emotional attention, and amygdala volume, all of which may relate to later socioemotional and behavioral development. The second aim was to investigate if maternal prenatal chronic psychological distress and chronic cortisol levels, which are measures of prenatal stress in this study, associate with infant fecal microbiota composition and diversity. The studies were conducted in the prospective, general population-based FinnBrain Birth Cohort Study. First, the results showed that early fecal microbiota composition was associated with temperament traits and attention bias towards fearful faces. Specifically, Bifidobacterium, Stroptococcus, and Atopobium were positively associated with positive emotionality, whereas Bifidobacterium was negatively and Clostridium was positively associated with greater attention bias towards fearful faces. Both temperament and attention bias towards faces showed an interaction by sex regarding fecal microbiota composition. The left amygdala volume as well as negative emotionality and fear reactivity were negatively associated with fecal microbiota diversity. Second, maternal prenatal stress associated with fecal microbiota composition, including increases in abundances of genera within the Proteobacteria phylum and decreases in Lactobacillus abundance. This dissertation argues that infant fecal microbiota associates with later brain and behavioral phenotypes and encourages future longitudinal and mechanistic studies. Likewise, we corroborate some earlier findings regarding maternal prenatal stress and infant fecal microbiota.Äidin raskaudenaikainen stressi, lapsen mikrobisto, käyttäytymisen ja aivojen kehitys Eläintöiden perusteella on ehdotettu, että suolistomikrobisto vaikuttaa aivojen toimintaan ja käyttäytymiseen. Lisäksi jyrsijöillä ja kädellisillä on osoitettu, että äidin raskaudenaikainen stressi vaikuttaa jälkeläisten kasvuun ja terveyteen sekä suolistomikrobiston koostumukseen. Vielä ei kuitenkaan täysin ymmärretä, että liittyykö äidin raskaudenaikainen stressi lapsen suolistomikrobiston koostumukseen tai liittyykö lapsen suolistomirkobiston koostumus varhaiseen käyttäytymiseen tai aivojen kehittymiseen ihmisillä. Tässä väitöskirjassa kartoitettiin FinnBrain-syntymäkohorttitutkimuksessa lapsen varhaisen mikrobiston yhteyksiä temperamenttiin, kasvoihin ja kasvojen ilmeisiin kohdistuvaan kognitiiviseen tarkkaavuuteen sekä mantelitumakkeen kokoon. Lisäksi väitöskirjassa tutkittiin äidin raskauden aikaisen pitkäaikaisten psyykkisten oireiden ja kortisolipitoisuuksien – joita käytettiin raskaudenaikaisen stressin mittareina tässä tutkimuksessa – yhteyksiä lapsen suolistomikrobiston koostumukseen ja monimuotoisuuteen. Lapsen suolistomikrobiston koostumus oli yhteydessä temperamenttiin ja varhaiseen pelokkaisiin kasvoihin kohdistuvaan tarkkaavaisuuteen. Bifidobacterium, Streptococcus, Atopobium bakteerisuvut olivat yhteydessä positiiviseen emotionaalisuuteen, ja toisaalta Clostridium ja Bifidobacterium suvut olivat yhteydessä pelokkaisiin kasvoisiin kohdistuvaan tarkkaavaisuuteen. Sukupuoli vaikutti suolistomikrobiston ja temperamentin sekä lapsen kasvoihin kohdistuvan tarkkaavaisuuden välisiin yhteyksiin. Suolistomikrobiston vähäisempi monimuotoisuus oli yhteydessä suurempaan vasemman mantelitumakkeen kokoon ja voimakkaampaan negatiiviseen emotionaalisuuteen ja pelkoreagoivuuteen. Äidin raskaudenaikaisten stressi oli yhteydessä Proteobakteereihin kuuluvien sukujen ja maitohappobakteerien pitoisuuksiin. Väitöskirjan löydökset tukevat väitettä, että suolistomikrobisto on yhteydessä aivojen kehitykseen ja käyttäytymiseen, mutta löydökset eivät vielä kerro taustalla olevista syy-seuraussuhteesta. Äidin raskaudenaikaisen stressin yhteydet lapsen suolistomikrobistoon vahvistivat jo aiemmin raportoituja löydöksiä

From data to science: a multi-Omics analysis of the pathobiome

Humans represent a complex ecosystem colonized not only by our cells but trillions of other microbes such as bacteria, archaea, fungi, and viruses. This microbiome gains increasing interest due to its involvement in human health and disease. While we live in symbiosis with most of these travellers, dysbiosis can lead to the growth of pathogens. Pathobionts are commensal microbes and harmless in healthy individuals until specific circumstances occur. There is increasing interest in studying this pathobiome due to the rise in infections with high mortality rates and stagnant treatment options. Due to the complexity of possible interactions between the host and microbes, studies on microbial interactions are conducted at varying scales. In this thesis, we start to study interactions in small, well-controlled model systems in vitro and then at the community level in vivo. The key technology used to identify, quantify, and characterize microbes and study host- microbe interactions throughout my studies is whole-genome and transcriptome sequencing. While an extensive body of work has focused on understanding the virulence factors of common pathogens, such as Aspergillus and Candida species, very little work has been done on understanding the interplay of those pathogens with the host’s symbionts or other pathogens at the start of my Ph.D. In my Ph.D. project, I used next- generation sequencing, advanced statistical approaches, and machine learning to significantly expanded our knowledge of the life of pathogens from an ecological point of view