Extracytoplasmic polysaccharides control cellulosomal and non-cellulosomal systems in Herbivorax saccincola A7.

Herbivorax saccincola A7 is an anaerobic alkali-thermophilic lignocellulolytic bacterium that possesses a cellulosome and high xylan degradation ability. To understand the expression profile of extracellular enzymes by carbon sources, quantitative real-time PCR was performed on all cellulosomal and non-cellulosomal enzyme genes of H. saccincola A7 using cellulose and xylan as carbon sources. The results confirmed that the scaffolding proteins of H. saccincola A7 were expressed. In general, the cellulosomal genes belonging to the glycoside hydrolase families 9, 10, 11, and 48 were repressed when xylan was the sole carbon source, but these genes were significantly induced in the presence of cellulose. These results indicate that cellulose, not xylan, is a key inducer of cellulosomal genes in H. saccincola A7. The RsgI-like proteins, which regulate a carbohydrate-sensing mechanism in Clostridium thermocellum, were also found to be encoded in the H. saccincola A7 genome. To confirm the regulation by RsgI-like proteins, the relative expression of σI1-σI4 factors was analyzed on both carbon sources. The expression of alternative σI1 and σI2 factors was enhanced by the presence of cellulose. By contrast, the expression of σI3 and σI4 factors was activated by both cellulose and xylan. Taken together, the results reveal that the cellulosomal and non-cellulosomal genes of H. saccincola A7 are regulated through a carbohydrate-sensing mechanism involving anti-σ regulator RsgI-like proteins. KEY POINTS: • qRT-PCR performed on cellulosomal and non-cellulosomal genes of H. saccincola A7 • Cellulose is a key inducer of the cellulosome of H. saccincola A7 • H. saccincola A7 possesses a similar system of anti-σ regulator RsgI-like proteins.

Structure Tailoring of Hemicyanine Dyes for In Vivo Shortwave Infrared Imaging.

In vivo bioimaging using shortwave infrared (SWIR) (1000-2000 nm) molecular dyes enables deeper penetration and higher contrast compared to visible and near-infrared-I (NIR-I, 700-900 nm) dyes. Developing new SWIR molecules is still quite challenging. This study developed SRHCYs, a panel of fluorescent dyes based on hemicyanine, with adjustable absorbance (830-1144 nm) and emission (886-1217 nm) wavelength. The photophysical attributes of these dyes are precisely tailored by strengthening the donor parts and extending polymethine chains. SRHCY-3, with its clickable azido group, was chosen for high-performance imaging of blood vessels in living mice, enabling the precise detection of brain and lung cancer. The combination of these probes achieved in vivo multicolor imaging with negligible optical crosstalk. This report presents a series of SWIR hemicyanine dyes with promising spectroscopic properties for high-contrast bioimaging and multiplexing detection.

MRDPDA: A multi-Laplacian regularized deepFM model for predicting piRNA-disease associations.

PIWI-interacting RNAs (piRNAs) are a typical class of small non-coding RNAs, which are essential for gene regulation, genome stability and so on. Accumulating studies have revealed that piRNAs have significant potential as biomarkers and therapeutic targets for a variety of diseases. However current computational methods face the challenge in effectively capturing piRNA-disease associations (PDAs) from limited data. In this study, we propose a novel method, MRDPDA, for predicting PDAs based on limited data from multiple sources. Specifically, MRDPDA integrates a deep factorization machine (deepFM) model with regularizations derived from multiple yet limited datasets, utilizing separate Laplacians instead of a simple average similarity network. Moreover, a unified objective function to combine embedding loss about similarities is proposed to ensure that the embedding is suitable for the prediction task. In addition, a balanced benchmark dataset based on piRPheno is constructed and a deep autoencoder is applied for creating reliable negative set from the unlabeled dataset. Compared with three latest methods, MRDPDA achieves the best performance on the pirpheno dataset in terms of the five-fold cross validation test and independent test set, and case studies further demonstrate the effectiveness of MRDPDA.

Erratum: Publisher Correction: Removal of the C4-domain preserves the drought tolerance enhanced by CsMYB4a and eliminates the negative impact of this transcription factor on plant growth.

[This corrects the article DOI: 10.1007/s42994-024-00149-5.].

Removal of the C4-domain preserves the drought tolerance enhanced by CsMYB4a and eliminates the negative impact of this transcription factor on plant growth.

The MYB4 transcription factor family regulates plant traits. However, their overexpression often results in undesirable side effects like growth reduction. We have reported a green tea (Camellia sinensis) MYB4 transcription factor (CsMYB4) that represses the phenylpropanoid and shikimate pathways and stunts plant growth and development. In the current study, we observed that in CsMYB4a transgenic tobacco (Nicotiana tabacum) plants, primary metabolism was altered, including sugar and amino acid metabolism, which demonstrated a pleiotropic regulation by CsMYB4a. The CsMYB4a transgenic tobacco plants had improved drought tolerance, which correlated to alterations in carbohydrate metabolism and an increase in proline content, as revealed by metabolic profiling and transcriptomic analysis. To mitigate the undesirable repressive side effects on plant traits, including dwarfism, shrunken leaves, and shorter roots of CsMYB4a transgenic plants, we deleted the C4 domain of CsMYB4a to obtain a CsMYB4a-DC4 variant and then overexpressed it in transgenic plants (CsMYB4a-DC4). These CsMYB4a-DC4 plants displayed a normal growth and had improved drought tolerance. Metabolite analysis demonstrated that the contents of carbohydrates and proline were increased in these transgenic plants. Our findings suggest that  an approriate modification of TFs can generate novel crop traits, thus providing potential agricultural benefits and expanding its application to various crops. Supplementary Information: The online version contains supplementary material available at 10.1007/s42994-024-00149-5.

Theoretical Insight into the Fluorescence Spectral Tuning Mechanism: A Case Study of Flavin-Dependent Bacterial Luciferase.

Bioluminescence of bacteria is widely applied in biological imaging, environmental toxicant detection, and many other situations. Understanding the spectral tuning mechanism not only helps explain the diversity of colors observed in nature but also provides principles for bioengineering new color variants for practical applications. In this study, time-dependent density functional theory (TD-DFT) and quantum mechanics and molecular mechanics (QM/MM) calculations have been employed to understand the fluorescence spectral tuning mechanism of bacterial luciferase with a focus on the electrostatic effect. The spectrum can be tuned by both a homogeneous dielectric environment and oriented external electric fields (OEEFs). Increasing the solvent polarity leads to a redshift of the fluorescence emission maximum, λF, accompanied by a substantial increase in density. In contrast, applying an OEEF along the long axis of the isoalloxazine ring (X-axis) leads to a significant red- or blue-shift in λF, depending on the direction of the OEEF, yet with much smaller changes in intensity. The effect of polar solvents is directionless, and the red-shifts can be attributed to the larger dipole moment of the S1 state compared with that of the S0 state. However, the effect of OEEFs directly correlates with the difference dipole moment between the S1 and S0 states, which is directional and is determined by the charge redistribution upon deexcitation. Moreover, the electrostatic effect of bacterial luciferase is in line with the presence of an internal electric field (IEF) pointing in the negative X direction. Finally, the key residues that contribute to this IEF and strategies for modulating the spectrum through site-directed point mutations are discussed.

Limb circumference measurements contributing to the diagnosis of snake venom-induced compartment syndrome.

Background: The present study aimed to investigate the relationship between swollen limb circumference and compartment pressure after a snakebite and to evaluate the diagnostic value of the circumference difference between the healthy and affected sides and the circumference growth rate for snake venom-induced compartment syndrome (CS). Method: The study was based on a prospective cohort study of snakebite patients at the emergency department of West China Hospital from May 2021 to October 2022. The snakebite patients were divided into the CS and non-compartment syndrome (NCS) groups. The diagnostic value of the circumference of the swollen limb for the CS after snakebite was evaluated using a receiver-operating characteristic curve analysis, and the cut-off value of the circumference of the swollen limb for CS after snakebite was calculated with sensitivity and specificity. Result: The present study enrolled 115 patients with severely swollen limbs after snakebite. The mean age was 59.1 ± 13.6 years, with 58 (50.4 %) female cases and 57 (49.6 %) male cases. There were 33 (28.7 %) cases where the upper limbs were injured and 82 (71.3 %) cases where the lower limbs were injured. These patients were divided into CS (n = 19) and NCS (n = 96) groups. The area under the curve (AUC) for the 15 cm circumference difference and circumferential growth rate of the upper edge of the patella was 0.683 (95 % CI 0.508 to 0.858, P = 0.037), and 0.685 (95 % CI 0.512 to 0.858, P = 0.035). The optimal cut-off values for the 15 cm circumference difference and circumferential growth rate of the upper edge of the patella to distinguish CS and NCS were 2.8 cm (sensitivity = 76.9 %, specificity = 66.7 %) and 7 % (sensitivity = 76.9 %, specificity = 66.7 %), respectively. Conclusion: Limb circumference measurement is a non-invasive, convenient, effective, and repeatable bedside test that can assist clinicians in the early detection of suspected snake venom-induced CS in patients exhibiting limb swelling after snake bites.

Assessment of short-term effects of ambient air pollution exposure on osteoarthritis outpatient visits.

The association of short-term ambient air pollution exposure with osteoarthritis (OA) outpatient visits has been unclear and no study has assessed the modifying roles of district-level characteristics in the association between ambient air pollution exposure and OA outpatient visits. We investigated the cumulative associations of ambient air pollution exposure with daily OA outpatient visits and vulnerable factors influencing the associations using data from 16 districts of Beijing, China during 2013-2019. A total of 18,351,795 OA outpatient visits were included in the analyses. An increase of 10 µg/m3 in fine particulate matter (PM2.5), inhalable particulate matter (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), maximum 8-hour moving-average ozone (8 h-O3), and 0.1 mg/m3 in carbon monoxide (CO) at representative lag days were associated with significant increases of 0.31 %, 0.06 %, 0.77 %, 0.87 %, 0.30 %, and 0.48 % in daily OA outpatient visits, respectively. Considerable OA outpatient visits were attributable to short-term ambient air pollution exposure. In addition, low temperature and high humidity aggravated ambient air pollution associated OA outpatient visits. District-level characteristics, such as population density, green coverage rate, and urbanization rate modified the risk of OA outpatient visits associated with air pollution exposure. These findings highlight the significance of controlling ambient air pollution during the urbanization process, which is useful in policy formation and implementation.

FPWT: Filter pruning via wavelet transform for CNNs.

The enormous data and computational resources required by Convolutional Neural Networks (CNNs) hinder the practical application on mobile devices. To solve this restrictive problem, filter pruning has become one of the practical approaches. At present, most existing pruning methods are currently developed and practiced with respect to the spatial domain, which ignores the potential interconnections in the model structure and the decentralized distribution of image energy in the spatial domain. The image frequency domain transform method can remove the correlation between image pixels and concentrate the image energy distribution, which results in lossy compression of images. In this study, we find that the frequency domain transform method is also applicable to the feature maps of CNNs. The filter pruning via wavelet transform (WT) is proposed in this paper (FPWT), which combines the frequency domain information of WT with the output feature map to more obviously find the correlation between feature maps and make the energy into a relatively concentrated distribution in the frequency domain. Moreover, the importance score of each feature map is calculated by the cosine similarity and the energy-weighted coefficients of the high and low frequency components, and prune the filter based on its importance score. Experiments on two image classification datasets validate the effectiveness of FPWT. For ResNet-110 on CIFAR-10, FPWT reduces FLOPs and parameters by more than 60.0 % with 0.53 % accuracy improvement. For ResNet-50 on ImageNet, FPWT reduces FLOPs by 53.8 % and removes parameters by 49.7 % with only 0.97 % loss of Top-1 accuracy.