Plasma Metabolomics Reveals Dysregulated Metabolic Signatures in HIV-Associated Immune Reconstitution Inflammatory Syndrome

Widely Targeted Metabolomic Analysis Revealed the Diversity in Milk from Goats, Sheep, Cows, and Buffaloes and Its Association with Flavor Profiles

The milk flavor can be attributed to the presence of numerous flavor molecules and precursors. In this study, we employed widely targeted metabolomic analysis techniques to analyze the metabolic profiles of various milk samples obtained from goats, sheep, dairy cows, and buffaloes. A total of 631 metabolites were identified in the milk samples, which were further categorized into 16 distinct classes. Principal component analysis (PCA) suggested that the metabolite profiles of samples from the same species exhibit clustering, while separated patterns of metabolite profiles are observed across goat, sheep, cow, and buffalo species. The differential metabolites between the groups of each species were screened based on fold change and variable importance in projection (VIP) values. Five core differential metabolites were subsequently identified, including 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid, inosine 5'-triphosphate, methylcysteine, N-cinnamylglycine, and small peptide (L-tyrosine-L-aspartate). Through multiple comparisons, we also screened biomarkers of each type of milk. Our metabolomic data showed significant inter-species differences in the composition and concentration of some compounds, such as organic acids, amino acids, sugars, nucleotides, and their derivatives, which may affect the overall flavor properties of the milk sample. These findings provided insights into the molecular basis underlying inter-species variations in milk flavor.

Epigenetic modification: A novel insight into diabetic wound healing

Wound healing is an intricate and fine regulatory process. In diabetic patients, advanced glycation end products (AGEs), excessive reactive oxygen species (ROS), biofilm formation, persistent inflammation, and angiogenesis regression contribute to delayed wound healing. Epigenetics, the fast-moving science in the 21st century, has been up to date and associated with diabetic wound repair. In this review, we go over the functions of epigenetics in diabetic wound repair in retrospect, covering transcriptional and posttranscriptional regulation. Among these, we found that histone modification is widely involved in inflammation and angiogenesis by affecting macrophages and endothelial cells. DNA methylation is involved in factors regulation in wound repair but also affects the differentiation phenotype of cells in hyperglycemia. In addition, noncodingRNA regulation and RNA modification in diabetic wound repair were also generalized. The future prospects for epigenetic applications are discussed in the end. In conclusion, the study suggests that epigenetics is an integral regulatory mechanism in diabetic wound healing.

Immune recovery uveitis: a focus review

Immune recovery uveitis (IRU) is an intraocular inflammation that typically occurs as part of immune reconstitution inflammatory syndrome (IRIS) in the eye. Typically, it affects human immunodeficiency virus (HIV)-infected patients with recognized or unrecognized cytomegalovirus (CMV) retinitis who are receiving highly active antiretroviral therapy (HAART). IRU is a common cause of new vision loss in these patients, and it manifests with a wide range of symptoms and an increased risk of inflammatory complications, such as macular edema. Recently, similar IRU-like responses have been observed in non-HIV individuals with immune reconstitution following immunosuppression of diverse etiologies, posing challenges in diagnosis and treatment. This review provides an updated overview of the current literature on the epidemiology, pathophysiology, biomarkers, clinical manifestations, diagnosis, differential diagnosis, and treatment strategies for IRU.

Epigenetic regulation of inflammation: The metabolomics connection

Epigenetic factors are considered the regulator of complex machinery behind inflammatory disorders and significantly contributed to the expression of inflammation-associated genes. Epigenetic modifications modulate variation in the expression pattern of target genes without affecting the DNA sequence. The current knowledge of epigenetic research focused on their role in the pathogenesis of various inflammatory diseases that causes morbidity and mortality worldwide. Inflammatory diseases are categorized as acute and chronic based on the disease severity and are regulated by the expression pattern of various genes. Hence, understanding the role of epigenetic modifications during inflammation progression will contribute to the disease outcomes and therapeutic approaches. This review also focuses on the metabolomics approach associated with the study of inflammatory disorders. Inflammatory responses and metabolic regulation are highly integrated and various advanced techniques are adopted to study the metabolic signature molecules. Here we discuss several metabolomics approaches used to link inflammatory disorders and epigenetic changes. We proposed that deciphering the mechanism behind the inflammation-metabolism loop may have immense importance in biomarkers research and may act as a principal component in drug discovery as well as therapeutic applications.

Network-based multi-omics integration reveals metabolic at-risk profile within treated HIV-infection

Multiomics technologies improve the biological understanding of health status in people living with HIV on antiretroviral therapy (PWH). Still, a systematic and in-depth characterization of metabolic risk profile during successful long-term treatment is lacking. Here, we used multi-omics (plasma lipidomic, metabolomic, and fecal 16 S microbiome) data-driven stratification and characterization to identify the metabolic at-risk profile within PWH. Through network analysis and similarity network fusion (SNF), we identified three groups of PWH (SNF-1-3): healthy (HC)-like (SNF-1), mild at-risk (SNF-3), and severe at-risk (SNF-2). The PWH in the SNF-2 (45%) had a severe at-risk metabolic profile with increased visceral adipose tissue, BMI, higher incidence of metabolic syndrome (MetS), and increased di- and triglycerides despite having higher CD4⁺ T-cell counts than the other two clusters. However, the HC-like and the severe at-risk group had a similar metabolic profile differing from HIV-negative controls (HNC), with dysregulation of amino acid metabolism. At the microbiome profile, the HC-like group had a lower α-diversity, a lower proportion of men having sex with men (MSM) and was enriched in Bacteroides. In contrast, in at-risk groups, there was an increase in Prevotella, with a high proportion of MSM, which could potentially lead to higher systemic inflammation and increased cardiometabolic risk profile. The multi-omics integrative analysis also revealed a complex microbial interplay of the microbiome-associated metabolites in PWH. Those severely at-risk clusters may benefit from personalized medicine and lifestyle intervention to improve their dysregulated metabolic traits, aiming to achieve healthier aging.

Human Immunodeficiency Virus and Uveitis

Uveitis is one of the most common ocular complications in people living with the Human immunodeficiency virus (HIV) and can be classified into HIV-induced uveitis, co-infection related uveitis, immune recovery uveitis, and drug-induced uveitis. The introduction of antiretroviral therapy has considerably changed the incidence, diagnosis, and treatment of different types of HIV-related uveitis. Furthermore, the specific immune condition of patients infected with HIV makes diagnosing HIV-related uveitis difficult. Recent studies have focused on the growing prevalence of syphilis/tuberculosis co-infection in uveitis. Simultaneously, more studies have demonstrated that HIV can directly contribute to the incidence of uveitis. However, the detailed mechanism has not been studied. Immune recovery uveitis is diagnosed by exclusion, and recent studies have addressed the role of biomarkers in its diagnosis. This review highlights recent updates on HIV-related uveitis. Furthermore, it aims to draw the attention of infectious disease physicians and ophthalmologists to the ocular health of patients infected with HIV.

Identifying novel host-based diagnostic biomarker panels for COVID-19: a whole-blood/nasopharyngeal transcriptome meta-analysis

BACKGROUND

Regardless of improvements in controlling the COVID-19 pandemic, the lack of comprehensive insight into SARS-COV-2 pathogenesis is still a sophisticated challenge. In order to deal with this challenge, we utilized advanced bioinformatics and machine learning algorithms to reveal more characteristics of SARS-COV-2 pathogenesis and introduce novel host response-based diagnostic biomarker panels.

METHODS

In the present study, eight published RNA-Seq datasets related to whole-blood (WB) and nasopharyngeal (NP) swab samples of patients with COVID-19, other viral and non-viral acute respiratory illnesses (ARIs), and healthy controls (HCs) were integrated. To define COVID-19 meta-signatures, Gene Ontology and pathway enrichment analyses were applied to compare COVID-19 with other similar diseases. Additionally, CIBERSORTx was executed in WB samples to detect the immune cell landscape. Furthermore, the optimum WB- and NP-based diagnostic biomarkers were identified via all the combinations of 3 to 9 selected features and the 2-phases machine learning (ML) method which implemented k-fold cross validation and independent test set validation.

RESULTS

The host gene meta-signatures obtained for SARS-COV-2 infection were different in the WB and NP samples. The gene ontology and enrichment results of the WB dataset represented the enhancement in inflammatory host response, cell cycle, and interferon signature in COVID-19 patients. Furthermore, NP samples of COVID-19 in comparison with HC and non-viral ARIs showed the significant upregulation of genes associated with cytokine production and defense response to the virus. In contrast, these pathways in COVID-19 compared to other viral ARIs were strikingly attenuated. Notably, immune cell proportions of WB samples altered in COVID-19 versus HC. Moreover, the optimum WB- and NP-based diagnostic panels after two phases of ML-based validation included 6 and 8 markers with an accuracy of 97% and 88%, respectively.

CONCLUSIONS

Based on the distinct gene expression profiles of WB and NP, our results indicated that SARS-COV-2 function is body-site-specific, although according to the common signature in WB and NP COVID-19 samples versus controls, this virus also induces a global and systematic host response to some extent. We also introduced and validated WB- and NP-based diagnostic biomarkers using ML methods which can be applied as a complementary tool to diagnose the COVID-19 infection from non-COVID cases.

Diagnosis and prognosis of COVID-19 employing analysis of patients' plasma and serum via LC-MS and machine learning

OBJECTIVE

To implement and evaluate machine learning (ML) algorithms for the prediction of COVID-19 diagnosis, severity, and fatality and to assess biomarkers potentially associated with these outcomes.

MATERIAL AND METHODS

Serum (n = 96) and plasma (n = 96) samples from patients with COVID-19 (acute, severe and fatal illness) from two independent hospitals in China were analyzed by LC-MS. Samples from healthy volunteers and from patients with pneumonia caused by other viruses (i.e. negative RT-PCR for COVID-19) were used as controls. Seven different ML-based models were built: PLS-DA, ANNDA, XGBoostDA, SIMCA, SVM, LREG and KNN.

RESULTS

The PLS-DA model presented the best performance for both datasets, with accuracy rates to predict the diagnosis, severity and fatality of COVID-19 of 93%, 94% and 97%, respectively. Low levels of the metabolites ribothymidine, 4-hydroxyphenylacetoylcarnitine and uridine were associated with COVID-19 positivity, whereas high levels of N-acetyl-glucosamine-1-phosphate, cysteinylglycine, methyl isobutyrate, l-ornithine and 5,6-dihydro-5-methyluracil were significantly related to greater severity and fatality from COVID-19.

CONCLUSION

The PLS-DA model can help to predict SARS-CoV-2 diagnosis, severity and fatality in daily practice. Some biomarkers typically increased in COVID-19 patients' serum or plasma (i.e. ribothymidine, N-acetyl-glucosamine-1-phosphate, l-ornithine, 5,6-dihydro-5-methyluracil) should be further evaluated as prognostic indicators of the disease.

Biotypes of HIV-associated neurocognitive disorders based on viral and immune pathogenesis

PURPOSE OF REVIEW

HIV-associated neurocognitive disorders (HAND) continues to be prevalent in people living with HIV despite antiretroviral therapy. However, understanding disease mechanisms and identifying therapeutic avenues has been challenging. One of the challenges is that HAND is a heterogeneous disease and that patients identified with similar impairments phenotypically may have very different underlying disease processes. As the NeuroAIDS field is re-evaluating the approaches used to identify patients with HIV-associated neurological impairments, we propose the subtyping of patients into biotypes based on viral and immune pathogenesis.

RECENT FINDINGS

Here we review the evidence supporting subtyping patients with HIV-associated neurological complications into four biotypes: macrophage-mediated HIV encephalitis, CNS viral escape, T-cell-mediated HIV encephalitis, and HIV protein-associated encephalopathy.

SUMMARY

Subtyping patients into subgroups based on biotypes has emerged as a useful approach for studying heterogeneous diseases. Understanding biotypes of HIV-associated neurocognitive impairments may therefore enable better understanding of disease mechanisms, allow for the development of prognostic and diagnostic markers, and could ultimately guide therapeutic decisions.