Molecular Network Analysis Reveals Transmission of HIV-1 Drug-Resistant Strains Among Newly Diagnosed HIV-1 Infections in a Moderately HIV Endemic City in China

Since the implementation of the “treat all” policy in China in 2016, there have been few data on the prevalence of transmitted drug resistance (TDR) in China. In this study, we describe TDR in patients newly diagnosed with human immunodeficiency virus (HIV) infection between 2016 and 2019 in Shenyang city, China. Demographic information and plasma samples from all newly reported HIV-infected individuals in Shenyang from 2016 to 2019 were collected. The HIV pol gene was amplified and sequenced for subtyping and TDR. The spread of TDR was analyzed by inferring an HIV molecular network based on pairwise genetic distance. In total, 2,882 sequences including CRF01_AE (2019/2,882, 70.0%), CRF07_BC (526/2,882, 18.3%), subtype B (132/2,882, 4.6%), and other subtypes (205/2,882, 7.1%) were obtained. The overall prevalence of TDR was 9.1% [95% confidence interval (CI): 8.1–10.2%]; the prevalence of TDR in each subtype in descending order was CRF07_BC [14.6% (95% CI: 11.7–18.0%)], subtype B [9.1% (95% CI: 4.8–15.3%)], CRF01_AE [7.9% (95% CI: 6.7–9.1%)], and other sequences [7.3% (95% CI: 4.2–11.8%)]. TDR mutations detected in more than 10 cases were Q58E (n = 51), M46ILV (n = 46), K103N (n = 26), E138AGKQ (n = 25), K103R/V179D (n = 20), and A98G (n = 12). Molecular network analysis revealed three CRF07_BC clusters with TDR [two with Q58E (29/29) and one with K103N (10/19)]; and five CRF01_AE clusters with TDR [two with M46L (6/6), one with A98G (4/4), one with E138A (3/3), and one with K103R/V179D (3/3)]. In the TDR clusters, 96.4% (53/55) of individuals were men who have sex with men (MSM). These results indicate that TDR is moderately prevalent in Shenyang (5–15%) and that TDR strains are mainly transmitted among MSM, providing precise targets for interventions in China.

Diversity and molecular network patterns of symptom phenotypes

Symptom phenotypes have continuously been an important clinical entity for clinical diagnosis and management. However, non-specificity of symptom phenotypes for clinical diagnosis is one of the major challenges that need be addressed to advance symptom science and precision health. Network medicine has delivered a successful approach for understanding the underlying mechanisms of complex disease phenotypes, which will also be a useful tool for symptom science. Here, we extracted symptom co-occurrences from clinical textbooks to construct phenotype network of symptoms with clinical co-occurrence and incorporated high-quality symptom-gene associations and protein–protein interactions to explore the molecular network patterns of symptom phenotypes. Furthermore, we adopted established network diversity measure in network medicine to quantify both the phenotypic diversity (i.e., non-specificity) and molecular diversity of symptom phenotypes. The results showed that the clinical diversity of symptom phenotypes could partially be explained by their underlying molecular network diversity (PCC = 0.49, P-value = 2.14E-08). For example, non-specific symptoms, such as chill, vomiting, and amnesia, have both high phenotypic and molecular network diversities. Moreover, we further validated and confirmed the approach of symptom clusters to reduce the non-specificity of symptom phenotypes. Network diversity proposes a useful approach to evaluate the non-specificity of symptom phenotypes and would help elucidate the underlying molecular network mechanisms of symptom phenotypes and thus promotes the advance of symptom science for precision health.

Molecular Network for Regulation of Ovule Number in Plants

In seed-bearing plants, the ovule (“small egg”) is the organ within the gynoecium that develops into a seed after fertilization. The gynoecium located in the inner compartment of the flower turns into a fruit. The number of ovules in the ovary determines the upper limit or the potential of seed number per fruit in plants, greatly affecting the final seed yield. Ovule number is an important adaptive characteristics for plant evolution and an agronomic trait for crop improvement. Therefore, understanding the mechanism and pathways of ovule number regulation becomes a significant research aspect in plant science. This review summarizes the ovule number regulators and their regulatory mechanisms and pathways. Specially, we construct the first integrated molecular network for ovule number regulation, in which phytohormones played a central role, followed by transcription factors, enzymes, other protein and micro-RNA. Of them, AUX, BR and CK are positive regulator of ovule number, whereas GA acts negatively on it. Interestingly, many ovule number regulators have conserved functions across several plant taxa, which should be the targets of genetic improvement via breeding or gene editing. Many ovule number regulators identified to date are involved in the diverse biological process, such as ovule primordia formation, ovule initiation, patterning, and morphogenesis. The relations between ovule number and related characteristics/traits especially of gynoecium/fruit size, ovule fertility, and final seed number, as well as upcoming research questions, are also discussed. In summary, this review provides a general overview of the present finding in ovule number regulation, which represents a more comprehensive and further cognition on it.

Epidemiology and Molecular Transmission Characteristics of HIV in the Capital City of Anhui Province in China

Hefei, Anhui province, is one of the cities in the Yangtze River Delta, where many people migrate to Jiangsu, Zhejiang and Shanghai. High migration also contributes to the HIV epidemic. This study explored the HIV prevalence in Hefei to provide a reference for other provinces and assist in the prevention and control of HIV in China. A total of 816 newly reported people with HIV in Hefei from 2017 to 2020 were recruited as subjects. HIV subtypes were identified by a phylogenetic tree. The most prevalent subtypes were CRF07_BC (41.4%), CRF01_AE (38.1%) and CRF55_01B (6.3%). Molecular networks were inferred using HIV-TRACE. The largest and most active transmission cluster was CRF55_01B in Hefei’s network. A Chinese national database (50,798 sequences) was also subjected to molecular network analysis to study the relationship between patients in Hefei and other provinces. CRF55_01B and CRF07_BC-N had higher clustered and interprovincial transmission rates in the national molecular network. People with HIV in Hefei mainly transmitted the disease within the province. Finally, we displayed the epidemic trend of HIV in Hefei in recent years with the dynamic change of effective reproductive number (Re). The weighted overall Re increased rapidly from 2012 to 2015, with a peak value of 3.20 (95% BCI, 2.18–3.85). After 2015, Re began to decline and remained stable at around 1.80. In addition, the Re of CRF55_01B was calculated to be between 2.0 and 4.0 in 2018 and 2019. More attention needs to be paid to the rapid spread of CRF55_01B and CRF07_BC-N strains among people with HIV and the high Re in Hefei. These data provide necessary support to guide the targeted prevention and control of HIV.

In Silico Identification and Clinical Validation of a Novel Long Non-Coding RNA/mRNA/miRNA Molecular Network for Potential Biomarkers for Discriminating SARS CoV-2 Infection Severity

(1) Background: The coronavirus (COVID-19) pandemic is still a major global health problem, despite the development of several vaccines and diagnostic assays. Moreover, the broad symptoms, from none to severe pneumonia, and the various responses to vaccines and the assays, make infection control challenging. Therefore, there is an urgent need to develop non-invasive biomarkers to quickly determine the infection severity. Circulating RNAs have been proven to be potential biomarkers for a variety of diseases, including infectious ones. This study aimed to develop a genetic network related to cytokines, with clinical validation for early infection severity prediction. (2) Methods: Extensive analyses of in silico data have established a novel IL11RA molecular network (IL11RNA mRNA, LncRNAs RP11-773H22.4 and hsa-miR-4257). We used different databases to confirm its validity. The differential expression within the retrieved network was clinically validated using quantitative RT-PCR, along with routine assessment diagnostic markers (CRP, LDH, D-dimmer, procalcitonin, Ferritin), in100 infected subjects (mild and severe cases) and 100 healthy volunteers. (3) Results: IL11RNA mRNA and LncRNA RP11-773H22.4, and the IL11RA protein, were significantly upregulated, and there was concomitant downregulation of hsa-miR-4257, in infected patients, compared to the healthy controls, in concordance with the infection severity. (4) Conclusion: The in-silico data and clinical validation led to the identification of a potential RNA/protein signature network for novel predictive biomarkers, which is in agreement with ferritin and procalcitonin for determination of COVID-19 severity.