BACKGROUND:Gut microbiome and genetic biomarkers are increasingly guiding obesity treatment. Bariatric surgery leads to shifts in gut microbial composition and function, while genome-wide association studies reveal genetic underpinnings of polygenic obesity, informing risk, therapeutic outcomes, and nutrigenomics-based interventions. OBJECTIVES:This pilot study aimed to identify gut microbiome and genetic biomarkers associated with weight loss 12 months after sleeve gastrectomy (SG). SETTING:Single academic institution university clinic. METHODS:Sixty-seven patients 12 months post-SG were enrolled: 34 had successful excess weight loss (EWL ≥50%), while 33 had unsuccessful EWL (EWL <50%). Microbiome and genetic profiles were collected and analyzed using ANOVA and regression methods. RESULTS:The genus Akkermansia was significantly associated with EWL (P = 9.9 × 10-6). Several microbial pathways, including propionate synthesis and menaquinone (vitamin K2) production, showed nominally significant (P < .05) associations with greater weight loss. No differences emerged in the Firmicutes/Bacteroidetes ratio. Genetic analyses revealed significant correlations between EWL and polygenic scores for dietary needs and metabolic responses, including distinct vitamin D and K requirements, as well as higher LDL cholesterol levels and predisposition for major depression. CONCLUSIONS:These findings suggest that both the gut microbiome and genetics may modulate weight loss following bariatric surgery. Integrating microbiome and genetic profiling into bariatric care pathways could enhance personalized obesity treatment. While this pilot, exploratory, and proof-of-concept study has limitations, it supports prior work linking gut microbial pathways to weight loss and suggests new associations. Follow-up studies are warranted to validate these results and further inform precision obesity interventions.

Background: Obesity, gastrointestinal disorders, and mental health conditions are major drivers of employer healthcare expenditures, yet nutrition-focused interventions are infrequently reimbursed by health insurance. Precision nutrition, which integrates genetic, gut microbiome, biometric, and behavioral data to guide personalized dietary and lifestyle changes, may offer a scalable approach to reducing costs associated with diet-responsive conditions. Objectives: To evaluate the impact of a precision nutrition digital therapeutic on employer medical spending for diet-responsive conditions in self-insured U.S. health plans. Methods: We conducted a retrospective cohort study of medical claims from January 2022 to December 2024 across seven U.S. self-insured employers. Employees enrolled in a precision nutrition digital therapeutic (n = 258) were compared with never-enrolled peers (n = 8268). We estimated treatment effects using a two-stage difference-in-differences model with member and calendar-month fixed effects and clustered standard errors, focusing on per-member-per-year (PMPY) employer-paid medical spending overall and for predefined diet-responsive condition categories. PMPY estimates were defined conditional on months with positive employer-paid spending and therefore reflect changes in the intensity of spending among members generating claims rather than unconditional per-capita costs. Results: Enrollment in the precision nutrition digital therapeutic was associated with a -$3012 PMPY reduction in diet-responsive medical spending (p = 0.021) relative to non-enrolled peers on this conditional basis. The largest relative reductions were observed for digestive disorders (-$9240 PMPY; p = 0.029) and obesity (-$4884 PMPY; p = 0.007), with a smaller reduction for anxiety-related conditions (-$1356 PMPY; p = 0.043). Total medical spending decreased by -$4044 PMPY but this change did not reach statistical significance (p = 0.09). Conclusions: In this multi-employer claims analysis, participation in a precision nutrition digital therapeutic was associated with lower employer-paid medical expenditures for diet-responsive conditions, particularly digestive disorders and obesity. These findings suggest that precision nutrition digital therapeutics may represent a scalable strategy for employers to address the economic burden of chronic disease within self-insured health plans by reducing the intensity of medical spending among members.

Obesity, gastrointestinal disorders, and mental health conditions are major drivers of employer healthcare expenditures, yet nutrition-based interventions are rarely reimbursed by health insurance. Precision nutrition, which integrates genetic, gut microbiome, biometric, and behavioral data to guide personalized dietary and lifestyle changes, may provide a scalable approach to reducing costs associated with diet-responsive conditions. This retrospective cohort study analyzed medical claims from January 2022 to December 2024 across seven U.S. self-insured employers. Employees enrolled in a precision nutrition digital therapeutic (n = 258) were compared with non-enrolled peers (n = 8,268) using a two-stage difference-in-differences model with member and month fixed effects and clustered standard errors. Enrollment was associated with a reduction of –$3,012 per member per year ( PMPY; p = 0.021) in diet-responsive medical spending. The largest relative reductions were observed for digestive disorders ( –$9,240 PMPY; p = 0.029) and obesity ( –$4,884 PMPY; p = 0.007), with smaller effects for anxiety-related conditions ( –$1,356 PMPY; p = 0.043). Total medical spending decreased by –$4,044 PMPY but was not statistically significant (p = 0.09). These results suggest that precision nutrition digital therapeutics may reduce medical expenditures for diet-responsive conditions and represent a scalable strategy for employers to address the economic burden of chronic disease.

IntroductionThe gut microbiome’s influence on weight management has gained significant interest for its potential to support better obesity therapeutics. Patient stratification leading to personalized nutritional intervention has shown benefits over one-size-fit-all diets. However, the efficacy and impact on the gut’s microbiome of personalizing weight loss diets based on individual factors remains under-investigated.MethodsThis study assessed the impact of Digbi Health’s personalized dietary and lifestyle program on weight loss and the gut microbiome end-points in 103 individuals. Participants’ weight loss patterns and gut microbiome profiles were analyzed from baseline to follow-up samples.ResultsSpecific microbial genera, functional pathways, and communities associated with BMI changes and the program’s effectiveness were identified. 80% of participants achieved weight loss. Analysis of the gut microbiome identified genera and functional pathways associated with a reduction in BMI, including Akkermansia, Christensenella, Oscillospiraceae, Alistipes, and Sutterella, short-chain fatty acid production, and degradation of simple sugars like arabinose, sucrose, and melibiose. Network analysis identified a microbiome community associated with BMI, which includes multiple taxa known for associations with BMI and obesity.DiscussionThe personalized dietary and lifestyle program positively impacted the gut microbiome and demonstrated significant associations between gut microbial changes and weight loss. These findings support the use of the gut microbiome as an endpoint in weight loss interventions, highlighting potential microbiome biomarkers for further research.

Our understanding of drug-microbe relationships has evolved from viewing microbes as mere drug producers to a dynamic, modifiable system where they can serve as drugs or targets of precision pharmacology. This review highlights recent findings on the gut microbiome, particularly focusing on four aspects of research: (i) drugs for bugs, covering recent strategies for targeting gut pathogens; (ii) bugs as drugs, including probiotics; (iii) drugs from bugs, including postbiotics; and (iv) bugs and drugs, discussing additional types of drug-microbe interactions. This review provides a perspective on future translational research, including efficient companion diagnostics in pharmaceutical interventions.

Obesity is a significant health problem due to its profound health deteriorating effects and high costs for healthcare systems. There exist lifestyle and pharmacological interventions available to prevent and reverse obesity; however, at the population level, these have shown to be insufficient, and we continue to see a worldwide increase in obesity prevalence. The gut microbiome has been shown to influence the susceptibility to weight gain and difficulty in losing weight and to be associated with successful long-term weight loss. Therefore, multiple studies have suggested that obesity interventions should consider the gut microbiome as a primary target through an improved diet and a crucial endpoint to monitor. However, there is a paucity of evidence regarding how to tailor the diet for an individual’s microbiome and what changes are expected to occur due to successful weight management. Digital therapeutics solutions have emerged as an exciting alternative to increase population access, reduce costs, and have the potential to accompany individuals on their health-promoting journey closely. Digbi Health has developed a dietary and lifestyle program to achieve weight loss that effectively reduces weight and improves diverse health outcomes by prioritizing and personalizing food ingredients to match an individual’s genetic profile and nurture the gut microbiome. In this study, we analyze the weight loss pattern and microbiome profile of 103 individuals to identify the effects of the weight loss program on the gut microbiome between their baseline and follow-up samples. We found that 80% of individuals lost weight during the study. Analysis of their gut microbiome identified genera, functional pathways, and microbial communities associated with BMI changes and dietary and lifestyle program. The microbial genera and functional pathways associated with a reduction in BMI during the study include several previously reported in the literature, including Akkermansia, Christensenella , Oscillospiraceae, Alistipes , and Sutterella , short-chain fatty acid (SCFA) production and degradation of simple sugars like arabinose, sucrose, and melibiose. Network analysis identified two microbiome communities associated with BMI, one of which also significantly responded to the weight loss program, which includes multiple known associations with BMI and obesity. Our findings provide additional evidence for using the gut microbiome as an endpoint of weight loss program and highlight how it positively impacts the gut microbiome, with significant parallels in weight loss and health outcomes. These results provide additional evidence for known microbiome biomarkers of obesity and highlight new ones that warrant further research.

Abstract The traditional network approach in gene expression analysis focuses on identifying relationships among gene expression patterns and associating them with metadata. In contrast, individualized coexpression networks offer a unique perspective by detecting underlying differences in gene expression datasets without known classifications, enabling sample stratification and providing insights into distinct phenotypes. This study aims to identify the strengths and limitations of two main approaches for generating individualized networks to study heterogeneous populations using easily accessible gene expression data. Two distinct families of methods were compared: the first method, based on statistical dependency and commonly used in single-cell genomics, successfully distinguished cell types in various datasets. The second approach, difference of correlation, has been applied to different cancer types and metabolic networks. We used two datasets with known classifications to experiment with scenarios where individualized networks apply. The evaluation involved neuronal cell types and breast cancer datasets. Our results revealed significant differences between the two approaches, suggesting dependencies on sample size, which determine the optimal method for specific applications. Through these evaluations, the dependency method demonstrated superior performance in capturing cell type variations and patient stratification compared to the difference correlation method. Nevertheless, both methods have limitations, including the need for multiple samples to assess correlations and potential challenges in handling heterogeneous sample groups. Individualized coexpression network methods offer valuable insights into biological systems, aiding in cell type classification, disease stratification, and personalized medicine applications. The results indicate their potential for precision medicine; however, researchers should consider their limitations and applicability to specific data types. Addressing sample diversity and size challenges will be pivotal in maximizing the benefits of individualized coexpression networks for future research and clinical applications.

This perspective highlights the potential of individualized networks as a novel strategy for studying complex diseases through patient stratification, enabling advancements in precision medicine. We emphasize the impact of interpatient heterogeneity resulting from genetic and environmental factors and discuss how individualized networks improve our ability to develop treatments and enhance diagnostics. Integrating system biology, combining multimodal information such as genomic and clinical data has reached a tipping point, allowing the inference of biological networks at a single-individual resolution. This approach generates a specific biological network per sample, representing the individual from which the sample originated. The availability of individualized networks enables applications in personalized medicine, such as identifying malfunctions and selecting tailored treatments. In essence, reliable, individualized networks can expedite research progress in understanding drug response variability by modeling heterogeneity among individuals and enabling the personalized selection of pharmacological targets for treatment. Therefore, developing diverse and cost-effective approaches for generating these networks is crucial for widespread application in clinical services.

Neuropsychiatric diseases and obesity are major components of morbidity and health care costs, with genetic, lifestyle, and gut microbiome factors linked to their etiology. Dietary and weight-loss interventions can help improve mental health, but there is conflicting evidence regarding their efficacy; and moreover, there is substantial interindividual heterogeneity that needs to be understood. We aimed to identify genetic and gut microbiome factors that explain interindividual differences in mental health improvement after a dietary and lifestyle intervention for weight loss. We recruited 369 individuals participating in Digbi Health’s personalized digital therapeutics care program and evaluated the association of 23 genetic scores, the abundance of 178 gut microbial genera, and 42 bacterial pathways with mental health. We studied the presence/absence of anxiety or depression, or sleep problems at baseline and improvement on anxiety, depression, and insomnia after losing at least 2% body weight. Participants lost on average 5.4% body weight and >95% reported improving mental health symptom intensity. There were statistically significant correlations between: (a) genetic scores with anxiety or depression at baseline, gut microbial functions with sleep problems at baseline, and (b) genetic scores and gut microbial taxa and functions with anxiety, depression, and insomnia improvement. Our results are concordant with previous findings, including the association between anxiety or depression at baseline with genetic scores for alcohol use disorder and major depressive disorder. As well, our results uncovered new associations in line with previous epidemiological literature. As evident from previous literature, we also observed associations of gut microbial signatures with mental health including short-chain fatty acids and bacterial neurotoxic metabolites specifically with depression. Our results also show that microbiome and genetic factors explain self-reported mental health status and improvement better than demographic variables independently. The genetic and microbiome factors identified in this study provide the basis for designing and personalizing dietary interventions to improve mental health.

Genome analysis of Bacillus safensis RP10, a strain from the soil of Atacama Desert in northern Chile, reflects a bacterium adapted to live in soil containing high levels of heavy metals, high salt conditions, and low carbon and energy sources.

We describe the characterization of the gastrointestinal tract (gut) and oral microbiota (bacteria) in 32 urban Pakistani adults representing seven major geographies and six ethnicities in the country. Study participants were between ages 18 and 40, had body mass index between 18 and 25 Kg/m2, and were early-career students or professionals belonging to 25 major cities of the country. These individuals donated a total of 61 samples (32 gut and 29 oral) that were subjected to 16S ribosomal RNA (rRNA) gene sequencing. Microbiome composition of Pakistani individuals was compared against the uBiome database of selected individuals who self-reported to be in excellent health. Using the crude measure of percentage overlap or similarity between the gut microbiota profile of Pakistani and uBiome dataset as proxy for health, our sequencing indicated that the Pakistani gut microbiota was moderately healthy relative to the uBiome dataset and Pakistani women appeared healthier relative to men. The Pakistani gut microbiome seemed susceptible to obesity and weight gain, levels of probiotics was very high likely due to the popularity of milk-based and fermented foods in the Pakistani diet, and bacteria that metabolize starch and carbohydrates (typically seen in the gut microbiota of honey bee) were abnormally enriched in the gut of Pakistani men. Our investigations reveal serious issues with the dietary habits and lifestyle of Pakistani individuals of consuming food enriched in high carbohydrates and fats, overcooked in oil and spices, following a sedentary lifestyle, little or no daily intake of fresh fruits, over-consumption of antibiotics from a very early age, and health and hygiene standards that do not meet international standards. Our sequencing is the first step towards generating a country-wide understanding of the impact of the local diet and lifestyle on Pakistani gut microbiota and can help understand its overall association with health and wellness.

In this report, we present a draft genome of 2,886,173bp of an Exiguobacterium aurantiacum strain PN47 isolate from the sediment of a saline pond named "Salar del Huasco" in the Altiplano in the North of Chile. Strain PN47 encodes adaptive characteristics enabling survival in extreme environmental conditions of high heavy metal and salt concentrations and high alkalinity.

Life-history transitions require major reprogramming at the behavioral and physiological level. Mating and reproductive maturation are known to trigger changes in gene transcription in reproductive tissues in a wide range of organisms, but we understand little about the molecular consequences of a failure to mate or become reproductively mature, and it is not clear to what extent these processes trigger neural as well as physiological changes. In this study, we examined the molecular processes underpinning the behavioral changes that accompany the major life-history transitions in a key pollinator, the bumblebee Bombus terrestris. We compared neuro-transcription in queens that succeeded or failed in switching from virgin and immature states, to mated and reproductively mature states. Both successes and failures were associated with distinct molecular profiles, illustrating how development during adulthood triggers distinct molecular profiles within a single caste of a eusocial insect. Failures in both mating and reproductive maturation were explained by a general up-regulation of brain gene transcription. We identified 21 genes that were highly connected in a gene coexpression network analysis: nine genes are involved in neural processes and four are regulators of gene expression. This suggests that negotiating life-history transitions involves significant neural processing and reprogramming, and not just changes in physiology. These findings provide novel insights into basic life-history transitions of an insect. Failure to mate or to become reproductively mature is an overlooked component of variation in natural systems, despite its prevalence in many sexually reproducing organisms, and deserves deeper investigation in the future.

The capacity to explore soft tissue structures in detail is important in understanding animal physiology and how this determines features such as movement, behaviour and the impact of trauma on regular function. Here we use advances in micro-computed tomography (micro-CT) technology to explore the brain of an important insect pollinator and model organism, the bumblebee (Bombus terrestris). Here we present a method for accurate imaging and exploration of insect brains that keeps brain tissue free from trauma and in its natural stereo-geometry, and showcase our 3D reconstructions and analyses of 19 individual brains at high resolution. Development of this protocol allows relatively rapid and cost effective brain reconstructions, making it an accessible methodology to the wider scientific community. The protocol describes the necessary steps for sample preparation, tissue staining, micro-CT scanning and 3D reconstruction, followed by a method for image analysis using the freeware SPIERS. These image analysis methods describe how to virtually extract key composite structures from the insect brain, and we demonstrate the application and precision of this method by calculating structural volumes and investigating the allometric relationships between bumblebee brain structures.

Genome-wide association studies (GWAS) have proven a valuable tool to explore the genetic basis of many traits. However, many GWAS lack statistical power and the commonly used single-point analysis method needs to be complemented to enhance power and interpretation. Multivariate region or gene-wide association are an alternative, allowing for identification of disease genes in a manner more robust to allelic heterogeneity. Gene-based association also facilitates systems biology analyses by generating a single p-value per gene. We have designed and implemented FORGE, a software suite which implements a range of methods for the combination of p-values for the individual genetic variants within a gene or genomic region. The software can be used with summary statistics (marker ids and p-values) and accepts as input the result file formats of commonly used genetic association software. When applied to a study of Crohn's disease susceptibility, it identified all genes found by single SNP analysis and additional genes identified by large independent meta-analysis. FORGE p-values on gene-set analyses highlighted association with the Jak-STAT and cytokine signalling pathways, both previously associated with CD. We highlight the software's main features, its future development directions and provide a comparison with alternative available software tools. FORGE can be freely accessed at https://github.com/inti/FORGE.

Genome-wide association studies (GWAS) have identified hundreds of loci at very stringent levels of statistical significance across many different human traits. However, it is now clear that very large samples (n~10^4-10^5) are needed to find the majority of genetic variants underlying risk for most human diseases. Therefore, the field has engaged itself in a race to increase study sample sizes with some studies yielding very successful results but also studies which provide little or no new insights. This project started early on in this new wave of studies and I decided to use an alternative approach that uses prior biological knowledge to improve both interpretation and power of GWAS. The project aimed to a) implement and develop new gene-based methods to derive gene-level statistics to use GWAS in well established system biology tools; b) use of these gene-level statistics in networks and gene-set analyses of GWAS data; c) mine GWAS of neuropsychiatric disorders using gene, gene-sets and integrative biology analyses with gene-expression studies; and d) explore the ability of these methods to improve the analysis GWAS on disease sub-phenotypes which usually suffer of very small sample sizes.

BACKGROUND:Despite high heritability, the genetic variants influencing bipolar disorder (BD) susceptibility remain largely unknown. Low statistical power to detect the small effect-size alleles believed to underlie much of the genetic risk and possible heterogeneity between cohorts are an increasing concern. Integrative biology approaches might offer advantages over genetic analysis alone by combining different genomic datasets at the higher level of biological processes rather than the level of specific genetic variants or genes. We employed this strategy to identify biological processes involved in BD etiopathology.METHOD:Three genome-wide association studies and a brain gene-expression study were combined with the Human Protein Reference Database protein-protein interaction network data. We used bioinformatic analysis to search for biological networks with evidence of association on the basis of enrichment among both genetic and differential-expression associations with BD.RESULTS:We identified association with gene networks involved in transmission of nerve impulse, Wnt, and Notch signaling. Three features stand out among these genes: 1) they localized to the human postsynaptic density, which is crucial for neuronal function; 2) their mouse knockouts present altered behavioral phenotypes; and 3) some are known targets of the pharmacological treatments for BD.CONCLUSIONS:Genetic and gene-expression associations of BD cluster in discrete regions of the protein-protein interaction network. We found replicated evidence for association for networks involving several interlinked signaling pathways. These genes are promising candidates to generate animal models and pharmacological interventions. Our results demonstrate the potential advantage of integrative biology analyses of BD datasets.