Non‐pollinating cheater wasps benefit from seasonally poor performance of the mutualistic pollinating wasps at the northern limit of the range of Ficus microcarpa

1. Species interactions in tightly bound ecological mutualisms often feature highly specialised species' roles in which competitive exclusion may preclude multi‐species coexistence. Among the 800 fig (Ficus) species, it was originally considered that each was pollinated by their own wasp (Agaonidae). However, recent investigations show that this ‘one‐to‐one’ rule often breaks down, as fig species regularly host multiple agaonids but in ways suggesting that competitive processes still mediate biodiversity outcomes. 2. A phenological survey was conducted of the fig–fig wasp pair, Ficus microcarpa and its associated pollinating wasp, alongside its sister species, the cheating wasp, in Xishuangbanna, China. 3. Reproductive output underwent extreme seasonal variation. Seed and pollinator production fell markedly during cooler, drier months, although high levels of fig production continued. However, this resource was predominantly utilised by the cheater species, which offers no pollination services. Pollinators and cheaters rarely co‐occur, suggesting that temporal coexistence is constrained by competition for access to figs. 4. The overall findings indicate periodic rearrangements of mutualism dynamics, probably resulting from a strongly seasonal environment. Sympatric co‐occurrence may result from a window of opportunity for a functionally divergent agaonid, potentially due to constraints on the main pollinator in adapting to variable year‐round conditions that prevent competitive exclusion.     

Association between FABP2 Ala54Thr polymorphisms and type 2 diabetes mellitus risk: a HuGE Review and Meta‐Analysis

Many studies have examined the association between the FABP2 (rs1799883) Ala54Thr gene polymorphism and type 2 diabetes mellitus risk (T2DM) in various populations, but their results have been inconsistent. To assess this relationship more precisely, A HuGE review and meta‐analysis were performed. The PubMed and CNKI database was searched for case‐control studies published up to April 2014. Data were extracted and pooled odds ratios (OR) with 95% confidence intervals (CI) were calculated. Ultimately, 13 studies, comprising 2020 T2DM cases and 2910 controls were included. Overall, for the Thr carriers (Ala/Thr and Thr/Thr) versus the wild‐type homozygotes (Ala/Ala), the pooled OR was 1.18 (95% CI = 1.04–1.34, P = 0.062 for heterogeneity), for Thr/Thr versus Ala/Ala the pooled OR was 1.17 (95% CI = 1.05–1.41 P = 0.087 for heterogeneity). In the stratified analysis by ethnicity, the significantly risks were found among Asians but not Caucasians. This meta‐analysis suggests that the FABP2 (rs1799883) Ala54Thr polymorphisms are associated with increased susceptibility to T2DM risk among Asians but not Caucasians.     

Cloning and characterization of a SnRK1-encoding gene from <Emphasis Type="Italic">Malus hupehensis</Emphasis> Rehd. and heterologous expression in tomato

Sucrose non-fermenting-1-related protein kinase-1 (SnRK1) plays an important role in metabolic regulation in plant. To understand the molecular mechanism of amino acids and carbohydrate metabolism in Malus hupehensis Rehd. var. pinyiensis Jiang (Pingyi Tiancha, PYTC), a full-length cDNA clone encoding homologue of SnRK1 was isolated from PYTC by Rapid Amplification of cDNA Ends (RACE). The clone, designated as MhSnRK1, contains 2063 nucleotides with an open reading frame of 1548 nucleotides. The deduced 515 amino acids showed high identities with other plant SnRK1 genes. Quantitative real-time PCR analysis revealed this gene was expressed in roots, stems and leaves. Exposing seedlings to nitrate caused and initial decrease in expression of the MhSnRK1 gene in roots, leaves and stems in short term. Ectopic expression of MhSnRK1 in tomato mainly resulted in higher starch content in leaf and red-ripening fruit than wild-type plants. This result supports the hypothesis that overexpression of SnRK1 causes the accumulation of starch in plant cells. All the results suggest that MhSnRK1 may play important roles in carbohydrate and amino acid metabolisms.     

N-糖蛋白去糖基化酶（PNGase）的研究进展

N-糖蛋白去糖基化酶（PNGase）是一种广泛存在于真菌、植物、哺乳动物中的去糖基化酶，可以水解N-糖蛋白或 N-糖肽上天冬酰胺与寡糖链连接的化学键，并释放出完整的N-寡糖。PNGase在生物体内参与蛋白质降解、器官发育、个体生长等过程。人PNGase基因功能缺陷会导致先天性去糖基化障碍，小鼠PNGase缺陷会导致胚胎致死性，线虫PNGase缺陷使其寿命下降。本文对PNGase在不同物种的分布、蛋白质结构、酶学功能及生物学功能进行阐述，为PNGase的生理病理功能及致病机制的基础研究提供思路，为PNGase作为糖生物学工具酶或药物开发的创新应用研究奠定基础。     

内蒙古部分地区骆驼和羊寄生蜱虫病毒组学分析

为了解内蒙古地区蜱虫病毒组学的本底数据,采用病毒宏基因组学方法对在内蒙古阿拉善盟左旗、右旗和四子王旗地区3个采样点采集骆驼和羊体表寄生的1789只蜱虫样品进行病毒宏基因组学分析,并对特定病毒进行巢式PCR扩增和测序,通过Clustal W和MEGA7.0等生物信息学软件对获得的病毒基因序列进行遗传进化分析.数据显示,蜱虫样品携带包括植物、脊椎动物和非脊椎动物等来源的17个病毒科和一些未分类的病毒;其中,2株弹状病毒具有丰富的遗传多样性,与新疆地区和长江地区的弹状病毒的同源性达到98.5％和96.26％,提示蜱虫弹状病毒可能是通过羊和骆驼等动物贸易导致了新疆和内蒙古地区,以及内地的跨区域传播;细小病毒仅在羊来源的蜱虫中检测到,与中国河北地区的山羊血清中的细小病毒形成同一进化分支,我们推测蜱虫细小病毒在国内不同地区间可跨区域传播,在进化分析过程中,发现这种病毒与多种的细小病毒的同源性都不低于50％,提示细小病毒可能具有遗传稳定性;Tamdy病毒与来自阿塞拜疆、乌兹别克斯坦和美国的Tamdy病毒均具有极高的同源性,结果显示该病毒在内蒙古地区已经出现,并存在潜在流行的可能,有必要对Tamdy病毒进行进一步的监测;在本研究中,我们鉴定的白蛉病毒与来自新疆的亚洲璃眼蜱所携带的博乐蜱虱病毒形成同一个进化分支,与新型布尼亚病毒和Heartland virus病毒的同源性达到50％以上,该结果提示,我们发现的蜱虫白蛉病毒可能具有潜在的致病性,需要对其流行情况和致病性进行监测和研究.本研究为完善内蒙古部分地区蜱虫病毒的多样性和本底情况提供了重要的基础数据.     

A cross-talk between epithelium and endothelium mediates human alveolar–capillary injury during SARS-CoV-2 infection

COVID-19, caused by SARS-CoV-2, is an acute and rapidly developing pandemic, which leads to a global health crisis. SARS-CoV-2 primarily attacks human alveoli and causes severe lung infection and damage. To better understand the molecular basis of this disease, we sought to characterize the responses of alveolar epithelium and its adjacent microvascular endothelium to viral infection under a co-culture system. SARS-CoV-2 infection caused massive virus replication and dramatic organelles remodeling in alveolar epithelial cells, alone. While, viral infection affected endothelial cells in an indirect manner, which was mediated by infected alveolar epithelium. Proteomics analysis and TEM examinations showed viral infection caused global proteomic modulations and marked ultrastructural changes in both epithelial cells and endothelial cells under the co-culture system. In particular, viral infection elicited global protein changes and structural reorganizations across many sub-cellular compartments in epithelial cells. Among the affected organelles, mitochondrion seems to be a primary target organelle. Besides, according to EM and proteomic results, we identified Daurisoline, a potent autophagy inhibitor, could inhibit virus replication effectively in host cells. Collectively, our study revealed an unrecognized cross-talk between epithelium and endothelium, which contributed to alveolar–capillary injury during SARS-CoV-2 infection. These new findings will expand our understanding of COVID-19 and may also be helpful for targeted drug development.Subject terms: Mechanisms of disease, Viral infection     

A study on giant panda recognition based on images of a large proportion of captive pandas

1. As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of conservation and management strategies, recognizing individual pandas is critical. However, it remains a challenging task because the existing methods, such as traditional tracking method, discrimination method based on footprint identification, and molecular biology method, are invasive, inaccurate, expensive, or challenging to perform. The advances of imaging technologies have led to the wide applications of digital images and videos in panda conservation and management, which makes it possible for individual panda recognition in a noninvasive manner by using image‐based panda face recognition method.
2. In recent years, deep learning has achieved great success in the field of computer vision and pattern recognition. For panda face recognition, a fully automatic deep learning algorithm which consists of a sequence of deep neural networks (DNNs) used for panda face detection, segmentation, alignment, and identity prediction is developed in this study. To develop and evaluate the algorithm, the largest panda image dataset containing 6,441 images from 218 different pandas, which is 39.78% of captive pandas in the world, is established.
3. The algorithm achieved 96.27% accuracy in panda recognition and 100% accuracy in detection.
4. This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behavior. This noninvasive approach is much more cost‐effective than the approaches used in the previous panda surveys.