mb-PHENIX: diffusion and supervised uniform manifold approximation for denoizing microbiota data.

MOTIVATION: Microbiota data encounters challenges arising from technical noise and the curse of dimensionality, which affect the reliability of scientific findings. Furthermore, abundance matrices exhibit a zero-inflated distribution due to biological and technical influences. Consequently, there is a growing demand for advanced algorithms that can effectively recover missing taxa while also considering the preservation of data structure. RESULTS: We present mb-PHENIX, an open-source algorithm developed in Python that recovers taxa abundances from the noisy and sparse microbiota data. Our method infers the missing information of count matrix (in 16S microbiota and shotgun studies) by applying imputation via diffusion with supervised Uniform Manifold Approximation Projection (sUMAP) space as initialization. Our hybrid machine learning approach allows to denoise microbiota data, revealing differential abundance microbes among study groups where traditional abundance analysis fails. AVAILABILITY AND IMPLEMENTATION: The mb-PHENIX algorithm is available at https://github.com/resendislab/mb-PHENIX. An easy-to-use implementation is available on Google Colab (see GitHub).

Dysbiosis signatures of gut microbiota and the progression of type 2 diabetes: a machine learning approach in a Mexican cohort.

Introduction: The gut microbiota (GM) dysbiosis is one of the causal factors for the progression of different chronic metabolic diseases, including type 2 diabetes mellitus (T2D). Understanding the basis that laid this association may lead to developing new therapeutic strategies for preventing and treating T2D, such as probiotics, prebiotics, and fecal microbiota transplants. It may also help identify potential early detection biomarkers and develop personalized interventions based on an individual's gut microbiota profile. Here, we explore how supervised Machine Learning (ML) methods help to distinguish taxa for individuals with prediabetes (prediabetes) or T2D. Methods: To this aim, we analyzed the GM profile (16s rRNA gene sequencing) in a cohort of 410 Mexican naïve patients stratified into normoglycemic, prediabetes, and T2D individuals. Then, we compared six different ML algorithms and found that Random Forest had the highest predictive performance in classifying T2D and prediabetes patients versus controls. Results: We identified a set of taxa for predicting patients with T2D compared to normoglycemic individuals, including Allisonella, Slackia, Ruminococus_2, Megaspgaera, Escherichia/Shigella, and Prevotella, among them. Besides, we concluded that Anaerostipes, Intestinibacter, Prevotella_9, Blautia, Granulicatella, and Veillonella were the relevant genus in patients with prediabetes compared to normoglycemic subjects. Discussion: These findings allow us to postulate that GM is a distinctive signature in prediabetes and T2D patients during the development and progression of the disease. Our study highlights the role of GM and opens a window toward the rational design of new preventive and personalized strategies against the control of this disease.

A network perspective on the ecology of gut microbiota and progression of type 2 diabetes: Linkages to keystone taxa in a Mexican cohort.

Introduction: The human gut microbiota (GM) is a dynamic system which ecological interactions among the community members affect the host metabolism. Understanding the principles that rule the bidirectional communication between GM and its host, is one of the most valuable enterprise for uncovering how bacterial ecology influences the clinical variables in the host. Methods: Here, we used SparCC to infer association networks in 16S rRNA gene amplicon data from the GM of a cohort of Mexican patients with type 2 diabetes (T2D) in different stages: NG (normoglycemic), IFG (impaired fasting glucose), IGT (impaired glucose tolerance), IFG + IGT (impaired fasting glucose plus impaired glucose tolerance), T2D and T2D treated (T2D with a 5-year ongoing treatment). Results: By exploring the network topology from the different stages of T2D, we observed that, as the disease progress, the networks lose the association between bacteria. It suggests that the microbial community becomes highly sensitive to perturbations in individuals with T2D. With the purpose to identify those genera that guide this transition, we computationally found keystone taxa (driver nodes) and core genera for a Mexican T2D cohort. Altogether, we suggest a set of genera driving the progress of the T2D in a Mexican cohort, among them Ruminococcaceae NK4A214 group, Ruminococcaceae UCG-010, Ruminococcaceae UCG-002, Ruminococcaceae UCG-005, Alistipes, Anaerostipes, and Terrisporobacter. Discussion: Based on a network approach, this study suggests a set of genera that can serve as a potential biomarker to distinguish the distinct degree of advances in T2D for a Mexican cohort of patients. Beyond limiting our conclusion to one population, we present a computational pipeline to link ecological networks and clinical stages in T2D, and desirable aim to advance in the field of precision medicine.

In Silico Identification and Characterization of circRNAs as Potential Virulence-Related miRNA/siRNA Sponges from Entamoeba histolytica and Encystment-Related circRNAs from Entamoeba invadens.

Ubiquitous eukaryotic non-coding circular RNAs regulate transcription and translation. We have reported full-length intronic circular RNAs (flicRNAs) in Entamoeba histolytica with esterified 3'ss and 5'ss. Their 5'ss GU-rich elements are essential for their biogenesis and their suggested role in transcription regulation. Here, we explored whether exonic, exonic-intronic, and intergenic circular RNAs are also part of the E. histolytica and E. invadens ncRNA RNAome and investigated their possible functions. Available RNA-Seq libraries were analyzed with the CIRI-full software in search of circular exonic RNAs (circRNAs). The robustness of the analyses was validated using synthetic decoy sequences with bona fide back splice junctions. Differentially expressed (DE) circRNAs, between the virulent HM1:IMSS and the nonvirulent Rahman E. histolytica strains, were identified, and their miRNA sponging potential was analyzed using the intaRNA software. Respectively, 188 and 605 reverse overlapped circRNAs from E. invadens and E. histolytica were identified. The sequence composition of the circRNAs was mostly exonic although different to human circRNAs in other attributes. 416 circRNAs from E. histolytica were virulent-specific and 267 were nonvirulent-specific. Out of the common circRNAs, 32 were DE between strains. Finally, we predicted that 8 of the DE circRNAs could function as sponges of the bioinformatically reported miRNAs in E. histolytica, whose functions are still unknown. Our results extend the E. histolytica RNAome and allow us to devise a hypothesis to test circRNAs/miRNAs/siRNAs interactions in determining the virulent/nonvirulent phenotypes and to explore other regulatory mechanisms during amoebic encystment.