Amyotrophic lateral sclerosis stratification: unveiling patterns with virome, inflammation, and metabolism molecules.

Amyotrophic lateral sclerosis (ALS) is an untreatable and clinically heterogeneous condition primarily affecting motor neurons. The ongoing quest for reliable biomarkers that mirror the disease status and progression has led to investigations that extend beyond motor neurons' pathology, encompassing broader systemic factors such as metabolism, immunity, and the microbiome. Our study contributes to this effort by examining the potential role of microbiome-related components, including viral elements, such as torque tenovirus (TTV), and various inflammatory factors, in ALS. In our analysis of serum samples from 100 ALS patients and 34 healthy controls (HC), we evaluated 14 cytokines, TTV DNA load, and 18 free fatty acids (FFA). We found that the evaluated variables are effective in differentiating ALS patients from healthy controls. In addition, our research identifies four unique patient clusters, each characterized by distinct biological profiles. Intriguingly, no correlations were found with site of onset, sex, progression rate, phenotype, or C9ORF72 expansion. A remarkable aspect of our findings is the discovery of a gender-specific relationship between levels of 2-ethylhexanoic acid and patient survival. In addition to contributing to the growing body of evidence suggesting altered peripheral immune responses in ALS, our exploratory research underscores metabolic diversity challenging conventional clinical classifications. If our exploratory findings are validated by further research, they could significantly impact disease understanding and patient care customization. Identifying groups based on biological profiles might aid in clustering patients with varying responses to treatments.

Bayesian Structure Learning in Multi-layered Genomic Networks.

Integrative network modeling of data arising from multiple genomic platforms provides insight into the holistic picture of the interactive system, as well as the flow of information across many disease domains including cancer. The basic data structure consists of a sequence of hierarchically ordered datasets for each individual subject, which facilitates integration of diverse inputs, such as genomic, transcriptomic, and proteomic data. A primary analytical task in such contexts is to model the layered architecture of networks where the vertices can be naturally partitioned into ordered layers, dictated by multiple platforms, and exhibit both undirected and directed relationships. We propose a multi-layered Gaussian graphical model (mlGGM) to investigate conditional independence structures in such multi-level genomic networks in human cancers. We implement a Bayesian node-wise selection (BANS) approach based on variable selection techniques that coherently accounts for the multiple types of dependencies in mlGGM; this flexible strategy exploits edge-specific prior knowledge and selects sparse and interpretable models. Through simulated data generated under various scenarios, we demonstrate that BANS outperforms other existing multivariate regression-based methodologies. Our integrative genomic network analysis for key signaling pathways across multiple cancer types highlights commonalities and differences of p53 integrative networks and epigenetic effects of BRCA2 on p53 and its interaction with T68 phosphorylated CHK2, that may have translational utilities of finding biomarkers and therapeutic targets.

Significant and Conflicting Correlation of IL-9 With Prevotella and Bacteroides in Human Colorectal Cancer.

Background and aim: Gut microbiota (GM) can support colorectal cancer (CRC) progression by modulating immune responses through the production of both immunostimulatory and/or immunosuppressive cytokines. The role of IL-9 is paradigmatic because it can either promote tumor progression in hematological malignancies or inhibit tumorigenesis in solid cancers. Therefore, we investigate the microbiota-immunity axis in healthy and tumor mucosa, focusing on the correlation between cytokine profile and GM signature. Methods: In this observational study, we collected tumor (CRC) and healthy (CRC-S) mucosa samples from 45 CRC patients, who were undergoing surgery in 2018 at the Careggi University Hospital (Florence, Italy). First, we characterized the tissue infiltrating lymphocyte subset profile and the GM composition. Subsequently, we evaluated the CRC and CRC-S molecular inflammatory response and correlated this profile with GM composition, using Dirichlet multinomial regression. Results: CRC samples displayed higher percentages of Th17, Th2, and Tregs. Moreover, CRC tissues showed significantly higher levels of MIP-1α, IL-1α, IL-1ß, IL-2, IP-10, IL-6, IL-8, IL-17A, IFN-γ, TNF-α, MCP-1, P-selectin, and IL-9. Compared to CRC-S, CRC samples also showed significantly higher levels of the following genera: Fusobacteria, Proteobacteria, Fusobacterium, Ruminococcus2, and Ruminococcus. Finally, the abundance of Prevotella spp. in CRC samples negatively correlated with IL-17A and positively with IL-9. On the contrary, Bacteroides spp. presence negatively correlated with IL-9. Conclusions: Our data consolidate antitumor immunity impairment and the presence of a distinct microbiota profile in the tumor microenvironment compared with the healthy mucosa counterpart. Relating the CRC cytokine profile with GM composition, we confirm the presence of bidirectional crosstalk between the immune response and the host's commensal microorganisms. Indeed, we document, for the first time, that Prevotella spp. and Bacteroides spp. are, respectively, positively and negatively correlated with IL-9, whose role in CRC development is still under debate.

Evaluation and comparison of short chain fatty acids composition in gut diseases.

BACKGROUND: An altered (dysbiosis) and unhealthy status of the gut microbiota is usually responsible for a reduction of short chain fatty acids (SCFAs) concentration. SCFAs obtained from the carbohydrate fermentation processes are crucial in maintaining gut homeostasis and their determination in stool samples could provide a faster, reliable and cheaper method to highlight the presence of an intestinal dysbiosis and a biomarker for various gut diseases. We hypothesize that different intestinal diseases, such as celiac disease (CD), adenomatous polyposis (AP) and colorectal cancer (CRC) could display a particular fecal SCFAs' signature. AIM: To compare the fecal SCFAs' profiles of CD, AP, CRC patients and healthy controls, using the same analytical method. METHODS: In this cross-sectional study, we defined and compared the SCFAs' concentration in fecal samples of 9 AP, 16 CD, 19 CRC patients and 16 healthy controls (HC). The SCFAs' analysis were performed using a gas-chromatography coupled with mass spectrometry method. Data analysis was carried out using Wilcoxon rank-sum test to assess pairwise differences of SCFAs' profiles, partial least squares-discriminate analysis (PLS-DA) to determine the status membership based on distinct SCFAs' profiles, and Dirichlet regression to determine factors influencing concentration levels of SCFAs. RESULTS: We have not observed any difference in the SCFAs' amount and composition between CD and healthy control. On the contrary, the total amount of SCFAs was significantly lower in CRC patients compared to HC (P = 0.044) and CD (P = 0.005). Moreover, the SCFAs' percentage composition was different in CRC and AP compared to HC. In detail, HC displayed higher percentage of acetic acid (P value = 1.3 × 10-6) and a lower amount of butyric (P value = 0.02192), isobutyric (P value = 7.4 × 10-5), isovaleric (P value = 0.00012) and valeric (P value = 0.00014) acids compared to CRC patients. AP showed a lower abundance of acetic acid (P value = 0.00062) and higher percentages of propionic (P value = 0.00433) and isovaleric (P value = 0.00433) acids compared to HC. Moreover, AP showed higher levels of propionic acid (P value = 0.03251) and a lower level of isobutyric acid (P value = 0.00427) in comparison to CRC. The PLS-DA model demonstrated a significant separation of CRC and AP groups from HC, although some degree of overlap was observed between CRC and AP. CONCLUSION: Analysis of fecal SCFAs shows the potential to provide a non-invasive means of diagnosis to detect patients with CRC and AP, while CD patients cannot be discriminated from healthy subjects.

On the estimation of a binary response model in a selected population.

A generalization of the Probit model is presented, with the extended skew-normal cumulative distribution as a link function, which can be used for modelling a binary response variable in the presence of selectivity bias. The estimate of the parameters via ML is addressed, and inference on the parameters expressing the degree of selection is discussed. The assumption underlying the model is that the selection mechanism influences the unmeasured factors and does not affect the explanatory variables. When this assumption is violated, but other conditional independencies hold, then the model proposed here is derived. In particular, the instrumental variable formula still applies and the model results at the second stage of the estimating procedure.