Advancing quantitative PCR with color cycle multiplex amplification.

Quantitative PCR (qPCR) is the gold standard for detection and quantitation of known DNA targets, but the scarcity of spectrally distinct fluorophores and filter sets limits the number of detectable targets. Here, we introduce color cycle multiplex amplification (CCMA) to significantly increase the number of detectable DNA targets in a single qPCR reaction using standard instrumentation. In CCMA, presence of one DNA target species results in a pre-programmed pattern of fluorescence increases. This pattern is distinguished by cycle thresholds (Cts) through rationally designed delays in amplification. For example, we design an assay wherein Staphylococcus aureus sequentially induces FAM, then Cy5.5, then ROX fluorescence increases with more than 3 cycles between each signal. CCMA offers notably higher potential for multiplexing because it uses fluorescence permutation rather than combination. With 4 distinct fluorescence colors, CCMA theoretically allows the detection of up to 136 distinct DNA target sequences using fluorescence permutation. Experimentally, we demonstrated a single-tube qPCR assay screening 21 sepsis-related bacterial DNA targets in samples of blood, sputum, pleural effusion and bronchoalveolar lavage fluid, with 89% clinical sensitivity and 100% clinical specificity, showing its potential as a powerful tool for advanced quantitative screening in molecular diagnostics.

Non-flooding conditions caused by water table drawdown alter microbial network complexity and decrease multifunctionality in alpine wetland soils.

Several soil functions of alpine wetland depend on microbial communities, including carbon storage and nutrient cycling, and soil microbes are highly sensitive to hydrological conditions. Wetland degradation is often accompanied by a decline in water table. With the water table drawdown, the effects of microbial network complexity on various soil functions remain insufficiently understood. In this research, we quantified soil multifunctionality of flooded and non-flooded sites in the Lalu Wetland on the Tibetan Plateau. We employed high-throughput sequencing to investigate the microbial community responses to water table depth changes, as well as the relationships between microbial network properties and soil multifunctionality. Our findings revealed a substantial reduction in soil multifunctionality at both surface and subsurface soil layers (0-20 cm and 20-40 cm) in non-flooded sites compared to flooded sites. The α-diversity of bacteria in the surface soil of non-flooded sites was significantly lower than that in flooded sites. Microbial network properties (including the number of nodes, number of edges, average degree, density, and modularity of co-occurrence networks) exhibited significant correlations with soil multifunctionality. This study underscores the adverse impact of non-flooded conditions resulting from water table drawdown on soil multifunctionality in alpine wetland soils, driven by alterations in microbial community structure. Additionally, we identified soil pH and moisture content as pivotal abiotic factors influencing soil multifunctionality, with microbial network complexity emerging as a valuable predictor of multifunctionality.

Soil temperature and fungal diversity jointly modulate soil heterotrophic respiration under short-term warming in the Zoige alpine peatland.

Global warming has changed carbon cycling in terrestrial ecosystems, but it remains unclear how climate warming affects soil heterotrophic respiration (Rh). We conducted a field experiment in the Zoige alpine peatland to investigate the mechanism of how short-term warming affects Rh by examining the relationships between plant biomass, soil properties, soil microbial diversity, and functional groups and Rh. Our results showed that warming increased Rh after one growing season of warming. However, warming barely changed the bacterial functional groups involved in the carbon cycle predicted by the functional annotation analysis. According to the Mantel test, NO3- was found to be the primary determinant for bacterial and fungal communities. The results of the Structural Equation Model (SEM) indicate that soil temperature and fungal diversity jointly modulate Rh, suggesting that short-term warming may not affect Rh by altering the structural and functional composition of microorganisms, which provides new insight into the mechanisms of the effects of warming on Rh in terrestrial ecosystems.

Single-Tube qPCR Detection and Quantitation of Hotspot Mutations Down to 0.01% Variant Allele Fraction.

Clinically and biologically, rare DNA sequence variants are significant and informative. However, existing common detection technologies are either complex and time-consuming in workflow, or restricted in the limit of detection (LoD), or do not allow for multiplexing. Blocker displacement amplification (BDA) method can stably and effectively detect and enrich multiple rare variants with LoD around 0.1% variant allele fraction (VAF). Nonetheless, the detailed mutation information has to be identified by additional sequencing technologies. Here, we present allele-specific BDA (As-BDA), a method combining BDA with allele-specific TaqMan (As-TaqMan) probes for effective variant enrichment and simultaneous single nucleotide variant or small insertions and deletions (INDELs) profiling. We demonstrated that As-BDA could detect mutations down to 0.01% VAF. Further, As-BDA could detect up to four mutations with low to 0.1% VAF per reaction using only 15 ng DNA input. The median error of As-BDA in VAF determination is approximately 9.1%. Comparison experiments using As-BDA and droplet digital PCR on peripheral blood mononuclear cell clinical samples showed 100% concordance for samples with mutations at ≥ 0.1% VAF. Hence, we have shown that As-BDA can achieve simultaneous enrichment and identification of multiple targeted mutations within the same reaction with high clinical sensitivity and specificity, thus helpful for clinical diagnosis.

Hairpin Structure Facilitates Multiplex High-Fidelity DNA Amplification in Real-Time Polymerase Chain Reaction.

Clinically and biologically, it is essential to detect rare DNA-sequence variants for early cancer diagnosis or drug-resistance mutation identification. Some of the common quantitative polymerase chain reaction (qPCR)-based variant detection methods are restricted in the limit of detection (LoD) because the DNA polymerases used for these methods have a high polymerase misincorporation rate; thus, the detection sensitivity is sometimes unsatisfactory. With the proofreading activity, high-fidelity (HiFi) DNA polymerases have a 50- to 250-fold higher fidelity. However, there are currently no proper probe-based designs functioning as the fluorescence indicator allowing multiplexed HiFi qPCR reactions, thus restricting the application of HiFi DNA polymerases like the variant detection. We presented the occlusion system, composed of a 5'-overhanged primer with a fluorophore modification and a probe with a short-stem hairpin and a 3' quencher modification. We demonstrated that the occlusion system allowed multiplexing HiFi qPCR reaction, and it was compatible with the current variant-enrichment method to improve the LoD up to 10-fold. Thus, the occlusion system satisfactorily functioned as an efficient fluorescence indicator in HiFi qPCR reactions and allowed the application of HiFi DNA polymerases in variant detection methods to improve detection sensitivity.

scFTD-seq: freeze-thaw lysis based, portable approach toward highly distributed single-cell 3' mRNA profiling.

Cellular barcoding of 3' mRNAs enabled massively parallel profiling of single-cell gene expression and has been implemented in droplet and microwell based platforms. The latter further adds the value for compatibility with low input samples, optical imaging, scalability, and portability. However, cell lysis in microwells remains challenging despite the recently developed sophisticated solutions. Here, we present scFTD-seq, a microchip platform for performing single-cell freeze-thaw lysis directly toward 3' mRNA sequencing. It offers format flexibility with a simplified, widely adoptable workflow that reduces the number of preparation steps and hands-on time, with the quality of data and cost per sample matching that of the state-of-the-art scRNA-seq platforms. Freeze-thaw, known as an unfavorable lysis method resulting in possible RNA fragmentation, turns out to be fully compatible with 3' scRNA-seq. We applied it to the profiling of circulating follicular helper T cells implicated in systemic lupus erythematosus pathogenesis. Our results delineate the heterogeneity in the transcriptional programs and effector functions of these rare pathogenic T cells. As scFTD-seq decouples on-chip cell isolation and library preparation, we envision it to allow sampling at the distributed sites including point-of-care settings and downstream processing at centralized facilities, which should enable wide-spread adoption beyond academic laboratories.

Changes in soil oxidase activity induced by microbial life history strategies mediate the soil heterotrophic respiration response to drought and nitrogen enrichment.

Drought and nitrogen deposition are two major climate challenges, which can change the soil microbial community composition and ecological strategy and affect soil heterotrophic respiration (Rh). However, the combined effects of microbial community composition, microbial life strategies, and extracellular enzymes on the dynamics of Rh under drought and nitrogen deposition conditions remain unclear. Here, we experimented with an alpine swamp meadow to simulate drought (50% reduction in precipitation) and multilevel addition of nitrogen to determine the interactive effects of microbial community composition, microbial life strategy, and extracellular enzymes on Rh. The results showed that drought significantly reduced the seasonal mean Rh by 40.07%, and increased the Rh to soil respiration ratio by 22.04%. Drought significantly altered microbial community composition. The ratio of K- to r-selected bacteria (BK:r) and fungi (FK:r) increased by 20 and 91.43%, respectively. Drought increased hydrolase activities but decreased oxidase activities. However, adding N had no significant effect on microbial community composition, BK:r, FK:r, extracellular enzymes, or Rh. A structural equation model showed that the effects of drought and adding nitrogen via microbial community composition, microbial life strategy, and extracellular enzymes explained 84% of the variation in Rh. Oxidase activities decreased with BK:r, but increased with FK:r. Our findings show that drought decreased Rh primarily by inhibiting oxidase activities, which is induced by bacterial shifts from the r-strategy to the K-strategy. Our results highlight that the indirect regulation of drought on the carbon cycle through the dynamic of bacterial and fungal life history strategy should be considered for a better understanding of how terrestrial ecosystems respond to future climate change.

Integrating major agricultural practices into the TRIPLEX-GHG model v2.0 for simulating global cropland nitrous oxide emissions: Development, sensitivity analysis and site evaluation.

Nitrous oxide (N2O) emissions from croplands are one of the most important greenhouse gas sources while the estimation of which remains large uncertainties globally. To simulate N2O emissions from global croplands, the process-based TRIPLEX-GHG model v2.0 was improved by coupling the major agricultural activities. Sensitivity experiment was used to measure the impact of the integrated processes to modeled N2O emission found chemical N fertilization have the highest relative effect sizes. While the coefficient of the NO3- consumption rate for denitrification (COEdNO3), controlling the first step of the denitrification process was identified to be the most sensitive parameter based on sensitivity analysis of model parameters. The model performed well when simulating the magnitude of the daily N2O emissions for 39 calibration sites and the continental mean of the parameters were used to producing reasonable estimations for the means of the measured daily N2O fluxes (R2 = 0.87, slope = 1.07) and emission factors (EFs, R2 = 0.70, slope = 0.72) during the experiment periods. The model reliability was further confirmed by model validation. General trend of modeled daily N2O emissions were reasonably consistent with the observations of selected validated sites. In addition, high correlations between the results of modeled and observed mean N2O emissions (R2 = 0.86, slope = 0.82) and EFs (R2 = 0.66, slope = 0.83) from 68 validation sites were obtained. Further improvement on more detailed estimations for the variation of the environmental factors, management effects as well as accurate model input model driving data are required to reduce the uncertainties of model simulations. Consequently, our simulation results demonstrate that the TRIPLEX-GHG model v2.0 can reliably estimate N2O emissions from various croplands at the global scale, which contributes to closing global N2O budget and sustainable development of agriculture.

Soil Water Content Shapes Microbial Community Along Gradients of Wetland Degradation on the Tibetan Plateau.

Soil microbes are important components in element cycling and nutrient supply for the development of alpine ecosystems. However, the development of microbial community compositions and networks in the context of alpine wetland degradation is unclear. We applied high-throughput 16S rRNA gene amplicon sequencing to track changes in microbial communities along degradation gradients from typical alpine wetland (W), to wet meadow (WM), to typical meadow (M), to grassland (G), and to desert (D) in the Zoige alpine wetland region on the Tibetan Plateau. Soil water content (SWC) decreased as wetland degradation progressed (79.4 and 9.3% in W and D soils, respectively). Total organic carbon (TOC), total nitrogen (TN), and total phosphorus (TP) increased in the soils of WM, and then decreased with alpine wetlands degradation from WM to the soils of M, G, and D, respectively. Wetland degradation did not affect microbial community richness and diversity from W soils to WM, M, and G soils, but did affect richness and diversity in D soils. Microbial community structure was strongly affected by wetland degradation, mainly due to changes in SWC, TOC, TN, and TP. SWC was the primary soil physicochemical property influencing microbial community compositions and networks. In wetland degradation areas, Actinobacteriota, Acidobacteriota, Cholorflexi, and Proteovacteria closely interacted in the microbial network. Compared to soils of W, WM, and M, Actinobacteriota played an important role in the microbial co-occurrence network of the G and D soils. This research contributes to our understanding of how microbial community composition and networks change with varied soil properties during degradation of different alpine wetlands.

High-Throughput Variant Detection Using a Color-Mixing Strategy.

Many diseases are related to multiple genetic alterations within a single gene. Probing for highly multiple (>10) variants in a single quantitative PCR tube is impossible because of a limited number of fluorescence channels and the limited ability to test one variant per channel, increasing the need for tubes. Herein, a novel color-mixing strategy was experimentally validated that uses fluorescence combinations as digital color codes to probe multiple variants simultaneously. The color-mixing strategy relies on a simple intratube assay that can probe for 15 variants as part of an intertube assay that can probe for an exponentially increased number of variants. This strategy is achieved by using multiplex double-stranded toehold probes modified with fluorophores and quenchers; the probes are designed to be quenched or remain luminous after binding to wild-type or variant templates. The color-mixing strategy was used to probe for 21 pathogenic variants in thalassemia and to distinguish between heterozygous and homozygous variants in six tubes, with a specificity of 99% and a sensitivity of 94%. To support tuberculosis diagnosis, the same strategy was applied to simultaneously probe in Mycobacterium tuberculosis for rifampicin-resistance mutations occurring within one 81-bp region and one 48-bp region in the rpoB gene, plus five isoniazid-resistance mutations in the inhA and katG genes.

Asynchronous responses of microbial CAZymes genes and the net CO2 exchange in alpine peatland following 5 years of continuous extreme drought events.

Peatlands act as an important sink of carbon dioxide (CO2). Yet, they are highly sensitive to climate change, especially to extreme drought. The changes in the net ecosystem CO2 exchange (NEE) under extreme drought events, and the driving function of microbial enzymatic genes involved in soil organic matter (SOM) decomposition, are still unclear. Herein we investigated the effects of extreme drought events in different periods of plant growth season at Zoige peatland on NEE and microbial enzymatic genes of SOM decomposition after 5 years. The results showed that the NEE of peatland decreased significantly by 48% and 26% on average (n = 12, P < 0.05) under the early and midterm extreme drought, respectively. The microbial enzymatic genes abundance of SOM decomposition showed the same decreasing trend under early and midterm extreme drought, but an increasing trend under late extreme drought. The microbial community that contributes to these degradation genes mainly derives from Proteobacteria and Actinobacteria. NEE was mainly affected by soil hydrothermal factors and gross primary productivity but weakly correlated with SOM enzymatic decomposition genes. Soil microbial respiration showed a positive correlation with microbial enzymatic genes involved in the decomposition of labile carbon (n = 18, P < 0.05). This study provided new insights into the responses of the microbial decomposition potential of SOM and ecosystem CO2 sink function to extreme drought events in the alpine peatland.

Changes in precipitation regime lead to acceleration of the N cycle and dramatic N2O emission.

Alpine meadows on the Qinghai-Tibetan Plateau are sensitive to climate change. The precipitation regime in this region has undergone major changes, "repackaging" precipitation from more frequent, smaller events to less frequent, larger events. Nitrous oxide (N2O) is an important indicator of responses to global change in alpine meadow ecosystems. However, little information is available describing the mechanisms driving the response of N2O emissions to changes in the precipitation regime. In this study, a manipulative field experiment was conducted to investigate N2O flux, soil properties, enzyme activity, and gene abundance in response to severe and moderate changes in precipitation regime over two years. Severe changes in precipitation regime led to a 12.6-fold increase in N2O fluxes (0.0068 ± 0.0018 mg m-2 h-1) from Zoige alpine meadows relative to natural conditions (0.0005 ± 0.0029 mg m-2 h-1). In addition, severe changes in precipitation regime significantly suppressed the activities of leucine amino peptidase (LAP) and peroxidase (PEO), affected ecoenzymatic stoichiometry, and increased the abundances of gdhA, narI and nirK genes, which significantly promoted organic nitrogen (N) decomposition, denitrification, and anammox processes. The increase in abundance of these genes could be ascribed to changes in the abundance of several dominant bacterial taxa (i.e., Actinobacteria and Proteobacteria) as a result of the altered precipitation regime. Decreases in nitrate and soil moisture caused by severe changes in precipitation may exacerbate N limitation and water deficit, lead to a suppression of soil enzyme activity, and change the structure of microorganism community. The N cycle of the alpine meadow ecosystem may accelerate by increasing the abundance of key N functional genes. This would, in turn, lead to increased N2O emission. This study provided insights into how precipitation regimes changes affect N cycling, and may also improve prediction of N2O fluxes in response to changes in precipitation regime.

Cost-Efficient Sequence-Based Nonextensible Oligonucleotide in Real-Time PCR and High-Throughput Sequencing.

Molecular detection of disease-associated mutations, especially those with low abundance, is essential for academic research and clinical diagnosis. Certain variant detection methods reach satisfactory sensitivity and specificity in detecting rare mutations based on the introduction of blocking oligos to prevent the amplification of wild-type or unwanted templates, thus selectively amplifying and enriching the mutations. These blocking oligos usually suppress PCR amplification through the 3' chemical modifications, with high price, slow synthesis, and reduced purity. Herein, we introduce chemistry-free designs to block enzymatic extension during PCR by the steric hindrance from the secondary structures attached to the 3' end of the oligos (nonextensible oligonucleotide, NEO). We demonstrated that NEO efficiently prohibited the extension of both Taq and high-fidelity DNA polymerases. By further applying NEO as blockers in blocker displacement amplification (BDA) qPCR, multiplex BDA (mBDA) NGS, and quantitative BDA (QBDA) NGS methods, we showed that NEO blockers had performance comparable with previously validated chemical modifications. Comparison experiments using QBDA with NEO blockers and droplet digital PCR (ddPCR) on clinical formalin-fixed paraffin-embedded (FFPE) samples exhibited 100% concordance. Lastly, the ability of NEO to adjust plex uniformity through changes of PCR amplification efficiency was demonstrated in an 80-plex NGS panel.

The divergent vertical pattern and assembly of soil bacterial and fungal communities in response to short-term warming in an alpine peatland.

Soil microbial communities are crucial in ecosystem-level decomposition and nutrient cycling processes and are sensitive to climate change in peatlands. However, the response of the vertical distribution of microbial communities to warming remains unclear in the alpine peatland. In this study, we examined the effects of warming on the vertical pattern and assembly of soil bacterial and fungal communities across three soil layers (0-10, 10-20, and 20-30 cm) in the Zoige alpine peatland under a warming treatment. Our results showed that short-term warming had no significant effects on the alpha diversity of either the bacterial or the fungal community. Although the bacterial community in the lower layers became more similar as soil temperature increased, the difference in the vertical structure of the bacterial community among different treatments was not significant. In contrast, the vertical structure of the fungal community was significantly affected by warming. The main ecological process driving the vertical assembly of the bacterial community was the niche-based process in all treatments, while soil carbon and nutrients were the main driving factors. The vertical structure of the fungal community was driven by a dispersal-based process in control plots, while the niche and dispersal processes jointly regulated the fungal communities in the warming plots. Plant biomass was significantly related to the vertical structure of the fungal community under the warming treatments. The variation in pH was significantly correlated with the assembly of the bacterial community, while soil water content, microbial biomass carbon/microbial biomass phosphorous (MBC/MBP), and microbial biomass nitrogen/ microbial biomass phosphorous (MBN/MBP) were significantly correlated with the assembly of the fungal community. These results indicate that the vertical structure and assembly of the soil bacterial and fungal communities responded differently to warming and could provide a potential mechanism of microbial community assembly in the alpine peatland in response to warming.

Divergent responses of CO2 and CH4 fluxes to changes in the precipitation regime on the Tibetan Plateau: Evidence from soil enzyme activities and microbial communities.

Carbon fluxes (CO2 and CH4) are important indicators of the response of alpine meadow ecosystems to global climate change. Alpine meadows on the Qinghai-Tibet Plateau are sensitive to climate change. Although the temporal allocation of precipitation can vary, its intensity is expected to increase, and its frequency is expected to decrease in the future. In this study, a manipulative field experiment was conducted to investigate how carbon fluxes are altered in response to moderate and severe changes in the precipitation regime. Fluctuations in CH4 flux were large under a severely altered precipitation regime (range of -0.048-0.038 mg m-2 h-1). Severe changes in the precipitation regime significantly reduced soil CH4 uptake by approximately 54.3%. This was probably affected by the decrease in the dissolved organic carbon concentration and changes in the microbial community (mainly Gammaproteobacteria), which were induced by variation in soil water conditions under various precipitation regimes. Under moderate changes in the precipitation regime, the average value of CO2 fluxes (ecosystem respiration) was 698.21 ± 35.19 mg m-2 h-1, which was significantly decreased by 20.7% compared with the control. This likely stems from the suppression of enzyme activity (particularly α-1,4-glucosidase and ß-1,4-glucosidase) and the alteration of microbial community structure in this treatment, which led to a decrease in organic matter breakdown and a reduction in the release of CO2 to the atmosphere. However, CO2 fluxes were slightly (i.e., not significantly) decreased under the severely altered precipitation regime. Such different responses of CO2 flux are probably driven by differences in microbial strategies. This study not only increases our understanding of the mechanisms underlying the adaptation of alpine meadow ecosystems to global climate change but also provides new insight into the carbon source/sink functions of alpine meadows.

Plant and Soil Enzyme Activities Regulate CO2 Efflux in Alpine Peatlands After 5 Years of Simulated Extreme Drought.

Increasing attention has been given to the impact of extreme drought stress on ecosystem ecological processes. Ecosystem respiration (Re) and soil respiration (Rs) play a significant role in the regulation of the carbon (C) balance because they are two of the largest terrestrial C fluxes in the atmosphere. However, the responses of Re and Rs to extreme drought in alpine regions are still unclear, particularly with respect to the driver mechanism in plant and soil extracellular enzyme activities. In this study, we imposed three periods of extreme drought events based on field experiments on an alpine peatland: (1) early drought, in which the early stage of plant growth occurred from June 18 to July 20; (2) midterm drought, in which the peak growth period occurred from July 20 to August 23; and (3) late drought, in which the wilting period of plants occurred from August 23 to September 25. After 5 years of continuous extreme drought events, Re exhibited a consistent decreasing trend under the three periods of extreme drought, while Rs exhibited a non-significant decreasing trend in the early and midterm drought but increased significantly by 58.48% (p < 0.05) during the late drought compared with the ambient control. Plant coverage significantly increased by 79.3% (p < 0.05) in the early drought, and standing biomass significantly decreased by 18.33% (p < 0.05) in the midterm drought. Alkaline phosphatase, polyphenol oxidase, and peroxidase increased significantly by 76.46, 77.66, and 109.60% (p < 0.05), respectively, under late drought. Structural equation models demonstrated that soil water content (SWC), pH, plant coverage, plant standing biomass, soil ß-D-cellobiosidase, and ß-1,4-N-acetyl-glucosaminidase were crucial impact factors that eventually led to a decreasing trend in Re, and SWC, pH, ß-1,4-glucosidase (BG), ß-1,4-xylosidase (BX), polyphenol oxidase, soil organic carbon, microbial biomass carbon, and dissolved organic carbon were crucial impact factors that resulted in changes in Rs. Our results emphasize the key roles of plant and soil extracellular enzyme activities in regulating the different responses of Re and Rs under extreme drought events occurring at different plant growth stages.

Headwater stream ecosystem: an important source of greenhouse gases to the atmosphere.

Although an increasing number of reports have revealed that rivers are important sources of greenhouse gases (GHGs), the magnitude and underlying mechanism of riverine GHG emissions are still poorly understood. The global extent of the headwater stream ecosystem may represent one of the important GHG emitters. A global database of GHG measurements from 595 rivers, indicated that the concentrations of riverine GHGs continually decrease as the stream order increases. Further analysis suggested that high GHG emissions from headwater streams (Strahler stream orders of 1 to 3) could be related to the low levels of dissolved oxygen, massive terrestrially derived carbon/nitrogen inputs and large gas exchange velocity. Through a combination of the predicted river surface areas and gas transfer velocities, we estimated that globally, the rivers emit approximately 6.6 (5.5-7.8) Pg CO2, 29.5 (19.6-37.3) Tg CH4, and 0.6 (0.2-0.9) Tg N2O per year, and totally emit 7.6 (6.1-9.1) CO2 equivalent into atmosphere per year. The headwater streams contribute 72.3%, 75.5%, and 77.2% of the global riverine CO2, CH4, and N2O emissions, respectively. This study presents a systematic estimation of GHG emissions from river ecosystems worldwide and highlights the dominant role played by headwater streams in GHG evasions from global rivers.

A deep learning model for predicting next-generation sequencing depth from DNA sequence.

Targeted high-throughput DNA sequencing is a primary approach for genomics and molecular diagnostics, and more recently as a readout for DNA information storage. Oligonucleotide probes used to enrich gene loci of interest have different hybridization kinetics, resulting in non-uniform coverage that increases sequencing costs and decreases sequencing sensitivities. Here, we present a deep learning model (DLM) for predicting Next-Generation Sequencing (NGS) depth from DNA probe sequences. Our DLM includes a bidirectional recurrent neural network that takes as input both DNA nucleotide identities as well as the calculated probability of the nucleotide being unpaired. We apply our DLM to three different NGS panels: a 39,145-plex panel for human single nucleotide polymorphisms (SNP), a 2000-plex panel for human long non-coding RNA (lncRNA), and a 7373-plex panel targeting non-human sequences for DNA information storage. In cross-validation, our DLM predicts sequencing depth to within a factor of 3 with 93% accuracy for the SNP panel, and 99% accuracy for the non-human panel. In independent testing, the DLM predicts the lncRNA panel with 89% accuracy when trained on the SNP panel. The same model is also effective at predicting the measured single-plex kinetic rate constants of DNA hybridization and strand displacement.

The significant contribution of lake depth in regulating global lake diffusive methane emissions.

Global lakes have been identified as an important component of natural methane (CH4) sources. Given that lake CH4 emissions involve multiple, complex processes influenced by various environmental factors, estimates of global lake CH4 emissions are largely uncertain. In this study, we compiled global CH4 emission data on 744 lakes from published studies, and found a significantly negative correlation (R2 = 0.50, p < 0.01) between diffusive CH4 flux and lake maximum depth. Further analysis indicated that no significant differences in global sediment CH4 production were found for the different maximum depths investigated. Owing to the longer oxidation pathway, presence of oxycline layer, and the lower nutrient environment, deeper lakes yield less diffusive CH4 efflux compared to shallower lakes. Additionally, we also found that lake area was negatively correlated (R2 = 0.13, p < 0.01) to diffusive CH4 flux. Therefore, based on empirical correlations between lake morphometry (maximum depth and area) and diffusive CH4 emission, as well as the combination of two lake databases, we estimated that the annual diffusive CH4 emission from global lakes is approximately 11.2 (6.2-19.5) Tg CH4/yr, and greater than 84% is emitted from lakes with a mean depth of less than 5 m. Furthermore, two regions, 40-70° N (30.4%) and 20° S∼10° N (37.4%), were found to be the dominant contributors of global lake diffusive CH4 emissions, resulting from the considerable total lake area and the extensive shallow lakes in these regions. This study highlights the significance of the 'depth-effect' which controls the spatial distribution of lake diffusive CH4 flux and allows for the quantification of global lake diffusive CH4 emissions.

Drought-induced reduction in methane fluxes and its hydrothermal sensitivity in alpine peatland.

Accurate estimation of CH4 fluxes in alpine peatland of the Qinghai-Tibetan Plateau under extreme drought is vital for understanding the global carbon cycle and predicting future climate change. However, studies on the impacts of extreme drought on peatland CH4 fluxes are limited. To study the effects of extreme drought on CH4 fluxes of the Zoige alpine peatland ecosystem, the CH4 fluxes during both extreme drought treatment (D) and control treatment (CK) were monitored using a static enclosed chamber in a control platform of extreme drought. The results showed that extreme drought significantly decreased CH4 fluxes in the Zoige alpine peatland by 31.54% (P < 0.05). Extreme drought significantly reduced the soil water content (SWC) (P < 0.05), but had no significant effect on soil temperature (Ts). Under extreme drought and control treatments, there was a significant negative correlation between CH4 fluxes and environmental factors (Ts and SWC), except Ts, at a depth of 5cm (P < 0.05). Extreme drought reduced the correlation between CH4 fluxes and environmental factors and significantly weakened the sensitivity of CH4 fluxes to SWC (P < 0.01). Moreover, it was found that the correlation between subsoil (20 cm) environmental factors and CH4 fluxes was higher than with the topsoil (5, 10 cm) environmental factors under the control and extreme drought treatments. These results provide a better understanding of the extreme drought effects on CH4 fluxes of alpine peatland, and their hydrothermal impact factors, which provides a reliable reference for peatland protection and management.