HMPDACC: a Human Microbiome Project Multi-omic data resource

SummaryPhylogenetic comparative methods are powerful but presently under-utilized ways to identify microbial genes underlying differences in community composition. These methods help to identify functionally important genes because they test for associations beyond those expected when related microbes occupy similar environments. We present phylogenize, a pipeline with web, QIIME 2 and R interfaces that allows researchers to perform phylogenetic regression on 16S amplicon and shotgun sequencing data and to visualize results. phylogenize applies broadly to both host-associated and environmental microbiomes. Using Human Microbiome Project and Earth Microbiome Project data, we show that phylogenize draws similar conclusions from 16S versus shotgun sequencing and reveals both known and candidate pathways associated with host colonization.Availability and implementation phylogenize is available at https://phylogenize.org and https://bitbucket.org/pbradz/phylogenize.Supplementary information Supplementary data are available at Bioinformatics online.

It is estimated that 422 million people around the world have diabetes mellitus (DM)—a devastating, complex, and highly heterogeneous disease—requesting better interventions based on disease subtyping. In this research, we utilize the discrete wavelet transform (DWT) to decompose and denoise DM data. Using DWT, we enhance heart rate variability (HRV) based DM diagnosis, data visualization of the disparities in Human Microbiome Project (HMP) data (gut bacteria, metabolomics, proteomics, RNA sequencing, targeted proteomics, and transcriptomics data) using demographic features, and insulin resistance prediction. We also attempt to forecast continuous glucose monitoring (CGM) ahead by 90 minutes because CGM is unable to provide real‐time blood glucose measurements. We achieve 91.9% diagnosis accuracy for Type 1 DM using Random Forest on data transformed with DWT, holding the potential for usage in clinics. In addition, our DWT‐based t‐SNE and UMAP explorative analysis of HMP data support subtypes of prediabetic patients stratified by sex, race, and age. Moreover, DWT‐based transformations provide multi‐view clustering that any other methods would not provide on metabolomics, proteomics, RNA sequencing, targeted proteomics, and transcriptomics data and outperform those without DWT. Taken together, DWT‐based machine learning approaches enable a fine resolution of subtyping DM towards precision medicine.

As many diseases are known to be related to microbes, interests in statistical methods for Microbiome-Wide Association Studies (MWAS) are also increasing. In this respect, we systematically investigate the properties of statistical methods for MWAS and compare their performances using simulation data generated from Human Microbiome Project data. We first assessed the type I error rates of eight commonly used methods over different levels of sparsity. Among these, ANCOM and metagenomeSeq methods yielded the well preserved type I error rates regardless of sparsity. In the power comparison study, metagenomeSeq showed higher power than ANCOM. In conclusion, we recommend using metagenomeSeq for the analysis of metagenome data.

The NIH Human Microbiome Project (HMP) has been carried out over ten years and two phases to provide resources, methods, and discoveries that link interactions between humans and their microbiomes to health-related outcomes. The recently completed second phase, the Integrative Human Microbiome Project, comprised studies of dynamic changes in the microbiome and host under three conditions: pregnancy and preterm birth; inflammatory bowel diseases; and stressors that affect individuals with prediabetes. The associated research begins to elucidate mechanisms of host–microbiome interactions under these conditions, provides unique data resources (at the HMP Data Coordination Center), and represents a paradigm for future multi-omic studies of the human microbiome.

Deposited here are human genome data comprising genotypes of 95 Human Microbiome Project participants (de-identified) at two SNPs, operational taxonomic unit (OTU) tables for metagenome assemblies from those individuals, and the R code used for for data analysis

Metagenomics, as a culture-independent approach, enables the exploration of complex heterogeneous microbial communities under natural conditions by massive sequencing of community-specific DNA. Metagenomic data sets, derived from various environments, provide new insights into microbial life. Large-scale projects like the Human Microbiome Project or the Earth Microbiome Project emphasize the increasing importance of metagenomics for biomedical and ecosystem research. However, such projects are currently challenging bioinformatics due to the explosive increase in sequencing data. New computationally efficient and statistically adequate methods are required to answer the essential questions “Who is in there?” and “What are they doing?”. In this thesis, I developed the Mixture-of-Pathways (MoP) model and Tax4Fun approach. Both methods link the taxonomic profile to a set of pre-computed reference profiles to predict the metabolic repertoire of the microbial community. Since the taxonomic profile is normally estimated to answer the question “Who is in there?”, the further use of the taxonomic profile avoids additional costs for answering the question “What are they doing?”. Tax4Fun is specifically designed for the output of 16S rRNA analysis pipelines using the SILVA database as reference, whereas the MoP model is especially conceived for metagenome sequence data and provides a robust statistical basis to describe the metabolic potential of a microbial community. The adequate metabolic modeling of metagenomes provides a concise summary of the functional variation of metagenomes across many samples, enabling the identification of relevant metabolic differences in comparative analyses. For comparative metagenomics, the identification of similar metagenomes to a newly obtained dataset is of growing importance. For an efficient large-scale identification of closely related metagenomes within a database retrieval context, I conducted a detailed evaluation of a k-nearest-neighbor search utilizing different biological feature profiles and metrics. I demonstrated that different features and metrics can be chosen for a convenient interpretation of results in terms of the underlying features. The integration of the k-nearest-neighbor search into metagenome annotation and comparison systems is beneficial to automatically identify additional metagenomes for comparative analyses as well as to detect mislabeled or contaminated datasets by unexpected neighboring habitat labels. The MoP approach and k-nearest-neighbor search are available to the scientific community as part of the CoMet-Universe web server application. Additionally, the MoP and Tax4Fun approach are provided as R Package.

The community ecology of microbial molecules.

Supplementation of all essential nutrients in daily diet is must, and omega-3 is the most crucial nutrient today. Anti-nutrients, pollutants should be avoided. Good nutrition helps to renew our body with healthy cells. Seemingly, opposite acting metabolism in both plants and animal life-forms works for creating reducing atmosphere, by deriving NADH and NADPH, reduced molecules, and by processing the food within plant cell and animal cell. Life is a process of defeating entropy, to keep the oxidative stress in check. Vegetarianism is healthier, naturally richer in antioxidants, the reduced molecules. Unlike plant forms, we are crippled in evolution and we need 40 odd essential nutrients to keep our body in a healthy state. Adequate supply of all the essential nutrients, at all times, is pivotal to keep all our faculties fit and fine till last. Although homeostasis keeps metabolites within normal limits, occasional drift by a process called allostasis may also be necessary by a way of readjustment. Redox seems to be playing a very vital role in every cell’s decision, to either remain in a healthy state of life, disease state or die or become immortal and assume a cancerous state.

Perivascular adipose tissue (PVAT) is a metabolically active, endocrine organ that plays a crucial role in regulating blood vessel tone, endothelial function, vascular smooth muscle cell growth, and proliferation and contributes significantly to the onset and progression of cardiovascular diseases. In a healthy state, PVAT displays anticontractile, anti-inflammatory, and antioxidative properties, which are critical for maintaining vascular homeostasis. However, under certain pathophysiological conditions, PVAT exerts pro-contractile effects by decreasing the production of anticontractile and/or increasing that of pro-contractile factors. In this context, recent studies have identified hydrogen sulfide (H2S) as a key vascular anti-contractile factor released from PVAT. The enzymes responsible for H2S biosynthesis are differentially expressed in PVAT, depending on the vascular bed and species, and their function can be altered by metabolic and cardiovascular diseases. These alterations can influence H2S signalling, further contributing to vascular dysfunction. PVAT-derived H2S may have particular importance in obesity-related vascular disease, hypertension, and diabetes as it has direct paracrine effects on the vasculature. Understanding the role of PVAT-derived H2S in both healthy and diseased states may provide new insights into preventing vascular dysfunction associated with PVAT changes. The dissection of the specific contributions of each enzyme involved in PVAT-derived H2S biosynthesis could be relevant to fully understanding the complex role of H2S in vascular health vs vascular disease. Further research into modulating PVAT-derived H2S provides an exciting avenue to explore novel pharmacological targets against vascular disease pathogenesis.

Acquired demyelination but not genetic developmental defects in myelination leads to brain tissue stiffness changes

In light of recent studies based on cultivation-independent methods, it appears that the diversity of Prevotella in human microbiota is greater than was previously assumed from cultivation-based studies, and that the implication of these bacteria in several human diseases was unrecognized. While some Prevotella taxa were found during opportunistic infections, changes in Prevotella abundance and diversity were discovered during dysbiosis-associated diseases. As member of the microbiota, Prevotella may also be considered as a reservoir for resistance genes. Greater knowledge on Prevotella diversity, as well as new insights into its pathogenic potential and implication in dysbiosis are expected from the use of human microbe identification microarrays, from whole-genome sequence analyse, and from the NIH Human Microbiome Project data. New approaches, including molecular-based methods, could contribute to improve the diagnosis of Prevotella infections.

Growth hormone (GH) is the key regulator of insulin-like growth factor I (IGF-I) generation in healthy states. However, portal insulin delivery is also an essential co-player in the regulation of the GH/IGF-I axis by affecting and regulating hepatic GH receptor synthesis, and subsequently altering hepatic GH sensitivity and IGF-I generation. Disease states of GH excess (e.g., acromegaly) and GH deficiency (e.g., congenital isolated GH deficiency) are characterized by increased and decreased GH, IGF-I and insulin levels, respectively, where the GH/IGF-I relationship is reflected by a "primary association". When intra-portal insulin levels are increased (e.g., obesity, Cushing's syndrome, or due to treatment with glucocorticoids and glucagon-like peptide 1 receptor agonists) or decreased (e.g., malnutrition, anorexia nervosa and type 1 diabetes mellitus), these changes secondarily alter hepatic GH sensitivity resulting in a "secondary association" with discordant GH and IGF-I levels (e.g., high GH/low IGF-I levels or low GH/high IGF-I levels, respectively). Additionally, intra-portal insulin regulates hepatic secretion of IGFBP-1, an inhibitor of IGF-I action. Through its effects on IGFBP-1 and subsequently free IGF-I, intra-portal insulin exerts its effects to influence endogenous GH secretion via the negative feedback loop. Therefore, it is important to understand the effects of changes in intra-portal insulin when interpreting the GH/IGF-I axis in disease states. This review summarizes our current understanding of how changes in intra-portal insulin delivery to the liver in health, disease states and drug therapy use and misuse that leads to alterations in GH/IGF-I secretion that may dictate management decisions in afflicted patients.

Estimation of endogenous glucose production after a glucose perturbation by nonparametric stochastic deconvolution

This paper first introduces the difference between human resources big data and human resources digital intelligence, and explains the three levels of human resources big data. Then it lists the problems that need to be solved in human resources management, namely, employment plan management, talent training and development, and incentive management of salary performance. Finally, it explains the path to achieve human resources digital intelligence. The results show that the realization of digital intelligence of human resources needs to be promoted through three stages, namely, stock data mining stage, sub data layer mining stage and digital intelligence management stage.

Acute kidney injury (AKI) affects up to 23.2% of hospitalized patients, but its complex pathophysiology hinders diagnosis and treatment. Bioinformatics-driven identification of core genes from large-scale omics data offers a promising approach for uncovering diagnostic and therapeutic targets. This study aims to integrate multi-omics data with experimental validation to identify core genes involved in AKI and explore their mechanisms. We analyzed renal transcriptomic data from 67 AKI patients and 20 controls, integrating differential expression, weighted gene co-expression network analysis (WGCNA), and clinical correlations to identify key genes. A nomogram model was used to assess diagnostic performance, and immune microenvironment characteristics were analyzed using CIBERSORT. AKI was induced in mice by ischemia-reperfusion and cisplatin, with gene expression validated by reverse transcription real-time quantitative polymerase chain reaction (RT-qPCR), Western blot, and immunohistochemistry. SUGCT expression and function were further examined in proximal tubules (PT) using human single-nucleus RNA sequencing (snRNA-seq) and spatial transcriptomics. Three core genes, epidermal growth factor (EGF), vascular cell adhesion molecule 1 (VCAM1), and SUGCT, were identified, showing significant associations with AKI phenotypes and clinical renal parameters. Combined, these genes provided a robust diagnostic model for AKI. CIBERSORT associated VCAM1 with monocytes, SUGCT with monocytes and M2 macrophages, and EGF with monocytes and T cells. Both mouse models showed downregulation of Sugct and Egf, and upregulation of Vcam1, consistent with human data. Single-nucleus RNA sequencing revealed that SUGCT was highly expressed in healthy PT but downregulated in severely injured PT. Low SUGCT expression correlated with suppressed mitochondrial functions and activated immune responses. Spatial transcriptomics confirmed that regions of high SUGCT expression co-localized with areas of oxidative phosphorylation activity in PT. This study highlights three core genes of AKI, especially SUGCT, which is related to mitochondrial metabolism and immune balance in PT during AKI, offering potential diagnostic and therapeutic targets.