Alteration of gastric microbiota in Helicobacter pylori-infected individuals suffering from GERD, DU, and gastritis.

BACKGROUND/AIM: Helicobacter pylori (H. pylori) colonization affects the gastric microbiome, causing gastrointestinal (GI) diseases. Modern sequencing technology provides insights into GI microbe interaction with H. pylori and their metabolic pathways in causing GI diseases. We aim to compare the gastric microbiota alteration due to H. pylori infection in patients suffering from GI diseases. MATERIALS AND METHODS: Genomic DNA were isolated from gastric antrum tissue from 37 H.pylori-infected patients diagnosed with GERD, duodenal ulcers, and gastritis. We conducted the genomic library preparation and sequencing of the amplified product using 16S rRNA NGS analysis. Using microbiome analyst tool diversity analysis, random forest analysis and ANOVA were conducted to find out the comparison of microbial abundance. We have also conducted functional pathway prediction analysis using PICRUSt. RESULTS: Metagenomic analysis shows high bacterial diversity in H. pylori-positive gastritis patients. Streptococcus infantis and Neisseria subflava were significantly higher in duodenal ulcer (DU) and gastritis groups. Acinetobacter lwoffii and Helicobacter pullorum were significantly high in the gastritis group only. The functional metabolic pathway analyses revealed that gastroesophageal reflux disease (GERD) samples were significantly enriched with the energy metabolism and xenobiotic biodegradation and metabolism pathways, whereas fructose-1,6-bisphosphatase III was found less in gastritis and DU groups. CONCLUSION: There is a difference in microbiota composition in different disease outcomes. We found positive association between microbial diversity and H. pylori in gastritis group only, whereas negative association was found in DU and GERD groups. The functional metabolic pathway analysis revealed significant differences in various disease outcomes.

Interactions between Human Gut Microbiome Dynamics and Sub-Optimal Health Symptoms during Seafaring Expeditions.

During long ocean voyages, crew members are subject to complex pressures from their living and working environment, which lead to chronic diseases-like sub-optimal health status. Although the association between dysbiotic gut microbiome and chronic diseases has been broadly reported, the correlation between the sub-optimal health status and gut microbiome remains elusive. Here, the health status of 77 crew members (20-35 years old Chinese, male) during a 135-day sea expedition was evaluated using the shotgun metagenomics of stool samples and health questionnaires taken before and after the voyage. We found five core symptoms (e.g., abnormal defecation frequency, insomnia, poor sleep quality, nausea, and overeating) in 55 out of 77 crew members suffering from sub-optimal health status, and this was termed "seafaring syndrome" (SS) in this study. Significant correlation was found between the gut microbiome and SS rather than any single symptom. For example, SS was proven to be associated with individual perturbation in the gut microbiome, and the microbial dynamics between SS and non-SS samples were different during the voyage. Moreover, the microbial signature for SS was identified using the variation of 19 bacterial species and 26 gene families. Furthermore, using a Random Forest model, SS was predicted with high accuracy (84.4%, area under the concentration-time curve = 0.91) based on 28 biomarkers from pre-voyage samples, and the prediction model was further validated by another 30-day voyage cohort (accuracy = 83.3%). The findings in this study provide insights to help us discover potential predictors or even therapeutic targets for dysbiosis-related diseases. IMPORTANCE Systemic and chronic diseases are important health problems today and have been proven to be strongly associated with dysbiotic gut microbiome. Studying the association between the gut microbiome and sub-optimal health status of humans in extreme environments (such as ocean voyages) will give us a better understanding of the interactions between observable health signs and a stable versus dysbiotic gut microbiome states. In this paper, we illustrated that ocean voyages could trigger different symptoms for different crew member cohorts due to individual differences; however, the co-occurrence of high prevalence symptoms indicated widespread perturbation of the gut microbiome. By investigating the microbial signature and gut microbiome dynamics, we demonstrated that such sub-optimal health status can be predicted even before the voyage. We termed this phenomenon as "seafaring syndrome." This study not only provides the potential strategy for health management in extreme environments but also can assist the prediction of other dysbiosis-related diseases.

Impact of comorbidity on patients with COVID-19 in India: A nationwide analysis.

Background: The emergence of coronavirus disease (COVID-19) as a global pandemic has resulted in the loss of many lives and a significant decline in global economic losses. Thus, for a large country like India, there is a need to comprehend the dynamics of COVID-19 in a clustered way. Objective: To evaluate the clinical characteristics of patients with COVID-19 according to age, gender, and preexisting comorbidity. Patients with COVID-19 were categorized according to comorbidity, and the data over a 2-year period (1 January 2020 to 31 January 2022) were considered to analyze the impact of comorbidity on severe COVID-19 outcomes. Methods: For different age/gender groups, the distribution of COVID-19 positive, hospitalized, and mortality cases was estimated. The impact of comorbidity was assessed by computing incidence rate (IR), odds ratio (OR), and proportion analysis. Results: The results indicated that COVID-19 caused an exponential growth in mortality. In patients over the age of 50, the mortality rate was found to be very high, ~80%. Moreover, based on the estimation of OR, it can be inferred that age and various preexisting comorbidities were found to be predictors of severe COVID-19 outcomes. The strongest risk factors for COVID-19 mortality were preexisting comorbidities like diabetes (OR: 2.39; 95% confidence interval (CI): 2.31-2.47; p < 0.0001), hypertension (OR: 2.31; 95% CI: 2.23-2.39; p < 0.0001), and heart disease (OR: 2.19; 95% CI: 2.08-2.30; p < 0.0001). The proportion of fatal cases among patients positive for COVID-19 increased with the number of comorbidities. Conclusion: This study concluded that elderly patients with preexisting comorbidities were at an increased risk of COVID-19 mortality. Patients in the elderly age group with underlying medical conditions are recommended for preventive medical care or medical resources and vaccination against COVID-19.

Predicting Selective RNA Processing and Stabilization Operons in Clostridium spp.

In selective RNA processing and stabilization (SRPS) operons, stem-loops (SLs) located at the 3'-UTR region of selected genes can control the stability of the corresponding transcripts and determine the stoichiometry of the operon. Here, for such operons, we developed a computational approach named SLOFE (stem-loop free energy) that identifies the SRPS operons and predicts their transcript- and protein-level stoichiometry at the whole-genome scale using only the genome sequence via the minimum free energy (ΔG) of specific SLs in the intergenic regions within operons. As validated by the experimental approach of differential RNA-Seq, SLOFE identifies genome-wide SRPS operons in Clostridium cellulolyticum with 80% accuracy and reveals that the SRPS mechanism contributes to diverse cellular activities. Moreover, in the identified SRPS operons, SLOFE predicts the transcript- and protein-level stoichiometry, including those encoding cellulosome complexes, ATP synthases, ABC transporter family proteins, and ribosomal proteins. Its accuracy exceeds those of existing in silico approaches in C. cellulolyticum, Clostridium acetobutylicum, Clostridium thermocellum, and Bacillus subtilis. The ability to identify genome-wide SRPS operons and predict their stoichiometry via DNA sequence in silico should facilitate studying the function and evolution of SRPS operons in bacteria.

Culture-Free Identification and Metabolic Profiling of Microalgal Single Cells via Ensemble Learning of Ramanomes.

Microalgae are among the most genetically and metabolically diverse organisms on earth, yet their identification and metabolic profiling have generally been slow and tedious. Here, we established a reference ramanome database consisting of single-cell Raman spectra (SCRS) from >9000 cells of 27 phylogenetically diverse microalgal species, each under stationary and exponential states. When combined, prequenching ("pigment spectrum" (PS)) and postquenching ("whole spectrum" (WS)) signals can classify species and states with 97% accuracy via ensemble machine learning. Moreover, the biosynthetic profile of Raman-sensitive metabolites was unveiled at single cells, and their interconversion was detected via intra-ramanome correlation analysis. Furthermore, not-yet-cultured cells from the environment were functionally characterized via PS and WS and then phylogenetically identified by Raman-activated sorting and sequencing. This PS-WS combined approach for rapidly identifying and metabolically profiling single cells, either cultured or uncultured, greatly accelerates the mining of microalgae and their products.

Pitchers of Nepenthes khasiana express several digestive-enzyme encoding genes, harbor mostly fungi and probably evolved through changes in the expression of leaf polarity genes.

BACKGROUND: A structural phenomenon seen in certain lineages of angiosperms that has captivated many scholars including Charles Darwin is the evolution of plant carnivory. Evidently, these structural features collectively termed carnivorous syndrome, evolved to aid nutritional acquisition from attracted, captured and digested prey. We now understand why plant carnivory evolved but how carnivorous plants acquired these attributes remains a mystery. In an attempt to understand the evolution of Nepenthes pitcher and to shed more light on its role in prey digestion, we analyzed the transcriptome data of the highly specialized Nepenthes khasiana leaf comprising the leaf base lamina, tendril and the different parts/zones of the pitcher tube viz. digestive zone, waxy zone and lid. RESULTS: In total, we generated around 262 million high-quality Illumina reads. Reads were pooled, normalized and de novo assembled to generate a reference transcriptome of about 412,224 transcripts. We then estimated transcript abundance along the N. khasiana leaf by mapping individual reads from each part/zone to the reference transcriptome. Correlation-based hierarchical clustering analysis of 27,208 commonly expressed genes indicated functional relationship and similar cellular processes underlying the development of the leaf base and the pitcher, thereby implying that the Nepenthes pitcher is indeed a modified leaf. From a list of 2386 differentially expressed genes (DEGs), we identified transcripts encoding key enzymes involved in prey digestion and protection against pathogen attack, some of which are expressed at high levels in the digestive zone. Interestingly, many of these enzyme-encoding genes are also expressed in the unopened N. khasiana pitcher. Transcripts showing homology to both bacteria and fungi were also detected; and in the digestive zone, fungi are more predominant as compared to bacteria. Taking cues from histology and scanning electron microscopy (SEM) photomicrographs, we found altered expressions of key regulatory genes involved in leaf development. Of particular interest, the expression of class III HOMEODOMAIN-LEUCINE ZIPPER (HD-ZIPIII) and ARGONAUTE (AGO) genes were upregulated in the tendril. CONCLUSIONS: Our findings suggest that N. khasiana pitchers employ a wide range of enzymes for prey digestion and plant defense, harbor microbes and probably evolved through altered expression of leaf polarity genes.