Transcriptome analysis unveiled the genetic basis of rapid seed germination strategies in alpine plant Rheum pumilum.

Rheum pumilum stands as both a quintessential alpine plant and a significant traditional Chinese and Tibetan medicinal herb. Unraveling the molecular intricacies of seed germination in Rh. pumilum not only unveils the genetic foundations of plant seed germination strategies in high-altitude environments but also offers insights for cultivating Rh. pumilum medicinal materials. Employing transcriptome sequencing and the Weighted Gene Co-expression Network Analysis, this study delved into the shifts in gene expression levels across various stages of seed germination in Rh. pumilum. The process of seed germination in Rh. pumilum entails a cascade of complex physiological events. Six hormones (ABA, IAA, ETH, GA, BR, CK) emerged as pivotal players in seeds breaking in shells and the facilitation of rapid seed germination in Rh. pumilum. Fourteen transcription factor families (LOB, GRAS, B3, bHLH, bZIP, EIL, MYB, MYB related, NAC, TCP, WRKY, HSF, PLATZ, and SBP) along with four key genes (E2.4.1.13, EIN3, BZR, and BIN2) were identified that may be associated with both biotic and abiotic environmental stress. The ETR, ACACA and ATPeV0C genes were linked with energy accumulation during the initial stages of seed germination, CYP707A may play an important role in breaking seed dormancy, while the BRI1 gene may be correlated with swift seed germination. Additionally, several unidentified genes were recognized to play key roles in seed germination of Rh. pumilum, warranting further investigation. Moreover, Rh. pumilum demonstrates full activation of crucial physiological functions such as energy metabolism, signal transduction, and responses to biological and abiotic stresses during the seed breaking in shells. This study provides molecular evidence elucidating the swift seed germination strategies adopted by alpine plants to thrive in high-altitude environments. Furthermore, it serves as a foundational reference for enhancing seed germination rates and breeding practices to promote the sustainable development of Rh. pumilum medicinal materials.

A single origin and high genetic diversity of cultivated medicinal herb Glehnia littoralis subsp. littoralis (Apiaceae) deciphered by SSR marker and phenotypic analysis.

Ten SSR markers based on transcriptome sequencing were employed to genotype 231 samples of G. littoralis subsp. littoralis (Apiaceae) from nine cultivated populations and seven wild populations, aiming to assess the genetic diversity and genetic structure, and elucidate the origin of the cultivated populations. Cultivated populations exhibited relatively high genetic diversity (h = 0.441, I = 0.877), slightly lower than that of their wild counterparts (h = 0.491, I = 0.930), likely due to recent domestication and ongoing gene flow between wild and cultivated germplasm. The primary cultivated population in Shandong have the crucial genetic status. A single origin of domestication was inferred through multiple analysis, and wild populations from Liaoning and Shandong are inferred to be potentially the ancestor source for the present cultivated populations. Phenotypic analysis revealed a relatively high heritability of root length across three growth periods (0.683, 0.284, 0.402), with significant correlations observed between root length and petiole length (Pearson correlation coefficient = 0.30, P<0.05), as well as between root diameter and leaf area (Pearson correlation coefficient = 0.36, P<0.01). These parameters can serve as valuable indicators for monitoring the developmental progress of medicinal plants during field management. In summary, this study can shed light on the intricate genetic landscape of G. littoralis subsp. littoralis, providing foundational insights crucial for conservation strategies, targeted breeding initiatives, and sustainable management practices in both agricultural and natural habitats.

Antimicrobial resistance and its risks evaluation in wetlands on the Qinghai-Tibetan Plateau.

Amidst the global antimicrobial resistance (AMR) crisis, antibiotic resistance has permeated even the most remote environments. To understand the dissemination and evolution of AMR in minimally impacted ecosystems, the resistome and mobilome of wetlands across the Qinghai-Tibetan Plateau and its marginal regions were scrutinized using metagenomic sequencing techniques. The composition of wetland microbiomes exhibits significant variability, with dominant phyla including Proteobacteria, Actinobacteria, Bacteroidetes, and Verrucomicrobia. Notably, a substantial abundance of Antibiotic Resistance Genes (ARGs) and Mobile Genetic Elements (MGEs) was detected, encompassing 17 ARG types, 132 ARG subtypes, and 5 types of MGEs (Insertion Sequences, Insertions Sequences, Genomic Islands, Transposons, and Integrative Conjugative Elements). No significant variance was observed in the prevalence of resistome and mobilome across different wetland types (i.e., the Yellow River, other rivers, lakes, and marshes) (R=-0.5882, P=0.607). The co-occurrence of 74 ARG subtypes and 22 MGEs was identified, underscoring the pivotal role of MGEs in shaping ARG pools within the Qinghai-Tibetan Plateau wetlands. Metagenomic binning and analysis of assembled genomes (MAGs) revealed that 93 out of 206 MAGs harbored ARGs (45.15 %). Predominantly, Burkholderiales, Pseudomonadales, and Enterobacterales were identified as the primary hosts of these ARGs, many of which represent novel species. Notably, a substantial proportion of ARG-carrying MAGs also contained MGEs, reaffirming the significance of MGEs in AMR dissemination. Furthermore, utilizing the arg_ranker framework for risk assessment unveiled severe contamination of high-risk ARGs across most plateau wetlands. Moreover, some prevalent human pathogens were identified as potential hosts for these high-risk ARGs, posing substantial transmission risks. This study aims to investigate the prevalence of resistome and mobilome in wetlands, along with evaluating the risk posed by high-risk ARGs. Such insights are crucial for informing environmental protection strategies and facilitating the management of water resources on the Qinghai-Tibetan Plateau.

Rapid discrimination of Bifidobacterium longum subspecies based on MALDI-TOF MS and machine learning.

Although MALDI-TOF mass spectrometry (MS) is widely known as a rapid and cost-effective reference method for identifying microorganisms, its commercial databases face limitations in accurately distinguishing specific subspecies of Bifidobacterium. This study aimed to explore the potential of MALDI-TOF MS protein profiles, coupled with prediction methods, to differentiate between Bifidobacterium longum subsp. infantis (B. infantis) and Bifidobacterium longum subsp. longum (B. longum). The investigation involved the analysis of mass spectra of 59 B. longum strains and 41 B. infantis strains, leading to the identification of five distinct biomarker peaks, specifically at m/z 2,929, 4,408, 5,381, 5,394, and 8,817, using Recurrent Feature Elimination (RFE). To facilate classification between B. longum and B. infantis based on the mass spectra, machine learning models were developed, employing algorithms such as logistic regression (LR), random forest (RF), and support vector machine (SVM). The evaluation of the mass spectrometry data showed that the RF model exhibited the highest performace, boasting an impressive AUC of 0.984. This model outperformed other algorithms in terms of accuracy and sensitivity. Furthermore, when employing a voting mechanism on multi-mass spectrometry data for strain identificaton, the RF model achieved the highest accuracy of 96.67%. The outcomes of this research hold the significant potential for commercial applications, enabling the rapid and precise discrimination of B. longum and B. infantis using MALDI-TOF MS in conjunction with machine learning. Additionally, the approach proposed in this study carries substantial implications across various industries, such as probiotics and pharmaceuticals, where the precise differentiation of specific subspecies is essential for product development and quality control.