KDM5B promotes immune evasion by recruiting SETDB1 to silence retroelements.

Tumours use various strategies to evade immune surveillance1,2. Immunotherapies targeting tumour immune evasion such as immune checkpoint blockade have shown considerable efficacy on multiple cancers3,4 but are ineffective for most patients due to primary or acquired resistance5-7. Recent studies showed that some epigenetic regulators suppress anti-tumour immunity2,8-12, suggesting that epigenetic therapies could boost anti-tumour immune responses and overcome resistance to current immunotherapies. Here we show that, in mouse melanoma models, depletion of KDM5B-an H3K4 demethylase that is critical for melanoma maintenance and drug resistance13-15-induces robust adaptive immune responses and enhances responses to immune checkpoint blockade. Mechanistically, KDM5B recruits the H3K9 methyltransferase SETDB1 to repress endogenous retroelements such as MMVL30 in a demethylase-independent manner. Derepression of these retroelements activates cytosolic RNA-sensing and DNA-sensing pathways and the subsequent type-I interferon response, leading to tumour rejection and induction of immune memory. Our results demonstrate that KDM5B suppresses anti-tumour immunity by epigenetic silencing of retroelements. We therefore reveal roles of KDM5B in heterochromatin regulation and immune evasion in melanoma, opening new paths for the development of KDM5B-targeting and SETDB1-targeting therapies to enhance tumour immunogenicity and overcome immunotherapy resistance.

stAA: adversarial graph autoencoder for spatial clustering task of spatially resolved transcriptomics.

With the development of spatially resolved transcriptomics technologies, it is now possible to explore the gene expression profiles of single cells while preserving their spatial context. Spatial clustering plays a key role in spatial transcriptome data analysis. In the past 2 years, several graph neural network-based methods have emerged, which significantly improved the accuracy of spatial clustering. However, accurately identifying the boundaries of spatial domains remains a challenging task. In this article, we propose stAA, an adversarial variational graph autoencoder, to identify spatial domain. stAA generates cell embedding by leveraging gene expression and spatial information using graph neural networks and enforces the distribution of cell embeddings to a prior distribution through Wasserstein distance. The adversarial training process can make cell embeddings better capture spatial domain information and more robust. Moreover, stAA incorporates global graph information into cell embeddings using labels generated by pre-clustering. Our experimental results show that stAA outperforms the state-of-the-art methods and achieves better clustering results across different profiling platforms and various resolutions. We also conducted numerous biological analyses and found that stAA can identify fine-grained structures in tissues, recognize different functional subtypes within tumors and accurately identify developmental trajectories.

Graph deep learning enabled spatial domains identification for spatial transcriptomics.

Advancing spatially resolved transcriptomics (ST) technologies help biologists comprehensively understand organ function and tissue microenvironment. Accurate spatial domain identification is the foundation for delineating genome heterogeneity and cellular interaction. Motivated by this perspective, a graph deep learning (GDL) based spatial clustering approach is constructed in this paper. First, the deep graph infomax module embedded with residual gated graph convolutional neural network is leveraged to address the gene expression profiles and spatial positions in ST. Then, the Bayesian Gaussian mixture model is applied to handle the latent embeddings to generate spatial domains. Designed experiments certify that the presented method is superior to other state-of-the-art GDL-enabled techniques on multiple ST datasets. The codes and dataset used in this manuscript are summarized at https://github.com/narutoten520/SCGDL.

Assembling spatial clustering framework for heterogeneous spatial transcriptomics data with GRAPHDeep.

MOTIVATION: Spatial clustering is essential and challenging for spatial transcriptomics' data analysis to unravel tissue microenvironment and biological function. Graph neural networks are promising to address gene expression profiles and spatial location information in spatial transcriptomics to generate latent representations. However, choosing an appropriate graph deep learning module and graph neural network necessitates further exploration and investigation. RESULTS: In this article, we present GRAPHDeep to assemble a spatial clustering framework for heterogeneous spatial transcriptomics data. Through integrating 2 graph deep learning modules and 20 graph neural networks, the most appropriate combination is decided for each dataset. The constructed spatial clustering method is compared with state-of-the-art algorithms to demonstrate its effectiveness and superiority. The significant new findings include: (i) the number of genes or proteins of spatial omics data is quite crucial in spatial clustering algorithms; (ii) the variational graph autoencoder is more suitable for spatial clustering tasks than deep graph infomax module; (iii) UniMP, SAGE, SuperGAT, GATv2, GCN, and TAG are the recommended graph neural networks for spatial clustering tasks; and (iv) the used graph neural network in the existent spatial clustering frameworks is not the best candidate. This study could be regarded as desirable guidance for choosing an appropriate graph neural network for spatial clustering. AVAILABILITY AND IMPLEMENTATION: The source code of GRAPHDeep is available at https://github.com/narutoten520/GRAPHDeep. The studied spatial omics data are available at https://zenodo.org/record/8141084.

Mimicking Tumor Metastasis Using a Transwell-Integrated Organoids-On-a-Chip Platform.

The mortality rate among cancer patients is primarily attributed to tumor metastasis. The evaluation of metastasis potential provides a powerful framework for personalized therapies. However, little work has so far been undertaken to precisely model tumor metastasis in vitro, hindering the development of preventive and therapeutic interventions. In this work, a tumor-metastasis-mimicked Transwell-integrated organoids-on-a-chip platform (TOP) for precisely evaluating tumor metastatic potential is developed. Unlike the conventional Transwell device for detecting cell migration, the engineered device facilitates the assessment of metastasis in patient-derived organoids (PDO). Furthermore, a novel Transwell chamber with a hexagon-shaped structure is developed to mimic the migration of tumor cells into surrounding tissues, allowing for the evaluation of tumor metastasis in a horizontal direction. As a proof-of-concept demonstration, tumor organoids and metastatic clusters are further evaluated at the protein, genetic, and phenotypic levels. In addition, preliminary drug screening is undertaken to highlight the potential for using the device to combat cancers. In summary, the tumor-metastasis-mimicked TOP offers unique capabilities for evaluating the metastasis potential of tumor organoids and contributes to the development of personalized cancer therapies.

Non-line-of-sight optical wireless communication system enabled by wavefront shaping for multi-user indoor access.

In this Letter, we experimentally investigate a non-line-of-sight (NLOS) optical wireless communication (OWC) system that utilizes wavefront shaping techniques to realize simultaneous data transmission for multiple users. Wavefront shaping techniques are employed to address the issue of low intensity of diffusely reflected light at the receiver in NLOS scenarios for indoor high-speed access. To achieve communication path planning and tracing for two different users in free-space optical communication, the pixels of the spatial light modulator (SLM) are divided into two halves to separately manipulate the wavefront of two independent data carriers centered at different wavelengths. The maximum received optical power can be effectively improved by more than 15 dB with the wavefront shaping technique. To avoid power enhancement of non-target wavelength, the wavelength difference of two different users is experimentally studied. The difference in power enhancement ratio (DPER) is increased with the wavelength difference, and 14.95 dB DPER is obtained with a 10 nm wavelength difference. Under the aforementioned wavelength planning strategy, successful transmission and reception of 2 × 160 Gbit/s 16-QAM signals for two users with coherent detection is achieved using wavelengths of 1550 and 1560 nm in an indoor access scenario.

Identification of CRKL as an oncogenic biomarker for prognosis and immunotherapy in melanoma, and its potential molecular mechanism.

CRKL (CRK Like Proto-Oncogene) belongs to the Crk family and is a 39-kDa adapter protein that encodes SH2 and SH3 (src homologs) domains. To identify its oncogenic role in malignant melanoma, we investigated the association between CRKL and mutation, prognosis, tumor mutation burden, immune cell infiltration of melanoma, and explored the associations between CRKL and immunotherapy response. Our results showed that abnormal CRKL expression is associated with poor prognosis in melanoma and is significantly correlated with immune-activated pathways and processes, immune cell infiltrations, and expression of immunoregulators. Importantly, we found that CRKL expression is a predictive biomarker for anti-PD1 therapy response in melanoma patients. Furthermore, inhibiting CRKL expression in melanoma cell lines suppressed their proliferation and metastasis, as well as activated the pyroptosis-related pathway. Our study provides potential mechanisms of melanoma pathogenesis, which may suggest new avenues for targeted therapy in this disease.

The role of TXNIP in cancer: a fine balance between redox, metabolic, and immunological tumor control.

Thioredoxin-interacting protein (TXNIP) is commonly considered a master regulator of cellular oxidation, regulating the expression and function of Thioredoxin (Trx). Recent work has identified that TXNIP has a far wider range of additional roles: from regulating glucose and lipid metabolism, to cell cycle arrest and inflammation. Its expression is increased by stressors commonly found in neoplastic cells and the wider tumor microenvironment (TME), and, as such, TXNIP has been extensively studied in cancers. In this review, we evaluate the current literature regarding the regulation and the function of TXNIP, highlighting its emerging role in modulating signaling between different cell types within the TME. We then assess current and future translational opportunities and the associated challenges in this area. An improved understanding of the functions and mechanisms of TXNIP in cancers may enhance its suitability as a therapeutic target.

HOXA1 participates in VSMC-to-macrophage-like cell transformation via regulation of NF-κB p65 and KLF4: a potential mechanism of atherosclerosis pathogenesis.

BACKGROUND: Macrophage-like transformation of vascular smooth muscle cells (VSMCs) is a risk factor of atherosclerosis (AS) progression. Transcription factor homeobox A1 (HOXA1) plays functional roles in differentiation and development. This study aims to explore the role of HOXA1 in VSMC transformation, thereby providing evidence for the potential mechanism of AS pathogenesis. METHODS: High fat diet (HFD)-fed apolipoprotein E knockout (ApoE-/-) mice were applied as an in vivo model to imitate AS, while 1-palmitoyl-2-(5-oxovaleroyl)-sn-glycero-3-phosphocholine (POV-PC)-treated VSMCs were applied as an in vitro model. Recombinant adeno-associated-virus-1 (AAV-1) vectors that express short-hairpin RNAs targeting HOXA1, herein referred as AAV1-shHOXA1, were generated for the loss-of-function experiments throughout the study. RESULTS: In the aortic root of AS mice, lipid deposition was severer and HOXA1 expression was higher than the wide-type mice fed with normal diet or HFD. Silencing of HOXA1 inhibited the AS-induced weight gain, inflammatory response, serum and liver lipid metabolism disorder and atherosclerotic plaque formation. Besides, lesions from AS mice with HOXA1 knockdown showed less trans-differentiation of VSMCs to macrophage-like cells, along with a suppression of krüppel-like factor 4 (KLF4) and nuclear factor (NF)-κB RelA (p65) expression. In vitro experiments consistently confirmed that HOXA1 knockdown suppressed lipid accumulation, VSMC-to-macrophage phenotypic switch and inflammation in POV-PC-treated VSMCs. Mechanism investigations further illustrated that HOXA1 transcriptionally activated RelA and KLF4 to participate in the pathological manifestations of VSMCs. CONCLUSIONS: HOXA1 participates in AS progression by regulating VSMCs plasticity via regulation of NF-κB p65 and KLF4. HOXA1 has the potential to be a biomarker or therapeutic target for AS.

QSAR-assisted-MMPA to expand chemical transformation space for lead optimization.

Matched molecular pairs analysis (MMPA) has become a powerful tool for automatically and systematically identifying medicinal chemistry transformations from compound/property datasets. However, accurate determination of matched molecular pair (MMP) transformations largely depend on the size and quality of existing experimental data. Lack of high-quality experimental data heavily hampers the extraction of more effective medicinal chemistry knowledge. Here, we developed a new strategy called quantitative structure-activity relationship (QSAR)-assisted-MMPA to expand the number of chemical transformations and took the logD7.4 property endpoint as an example to demonstrate the reliability of the new method. A reliable logD7.4 consensus prediction model was firstly established, and its applicability domain was strictly assessed. By applying the reliable logD7.4 prediction model to screen two chemical databases, we obtained more high-quality logD7.4 data by defining a strict applicability domain threshold. Then, MMPA was performed on the predicted data and experimental data to derive more chemical rules. To validate the reliability of the chemical rules, we compared the magnitude and directionality of the property changes of the predicted rules with those of the measured rules. Then, we compared the novel chemical rules generated by our proposed approach with the published chemical rules, and found that the magnitude and directionality of the property changes were consistent, indicating that the proposed QSAR-assisted-MMPA approach has the potential to enrich the collection of rule types or even identify completely novel rules. Finally, we found that the number of the MMP rules derived from the experimental data could be amplified by the predicted data, which is helpful for us to analyze the medicinal chemical rules in local chemical environment. In summary, the proposed QSAR-assisted-MMPA approach could be regarded as a very promising strategy to expand the chemical transformation space for lead optimization, especially when no enough experimental data can support MMPA.

PySmash: Python package and individual executable program for representative substructure generation and application.

BACKGROUND: Substructure screening is widely applied to evaluate the molecular potency and ADMET properties of compounds in drug discovery pipelines, and it can also be used to interpret QSAR models for the design of new compounds with desirable physicochemical and biological properties. With the continuous accumulation of more experimental data, data-driven computational systems which can derive representative substructures from large chemical libraries attract more attention. Therefore, the development of an integrated and convenient tool to generate and implement representative substructures is urgently needed. RESULTS: In this study, PySmash, a user-friendly and powerful tool to generate different types of representative substructures, was developed. The current version of PySmash provides both a Python package and an individual executable program, which achieves ease of operation and pipeline integration. Three types of substructure generation algorithms, including circular, path-based and functional group-based algorithms, are provided. Users can conveniently customize their own requirements for substructure size, accuracy and coverage, statistical significance and parallel computation during execution. Besides, PySmash provides the function for external data screening. CONCLUSION: PySmash, a user-friendly and integrated tool for the automatic generation and implementation of representative substructures, is presented. Three screening examples, including toxicophore derivation, privileged motif detection and the integration of substructures with machine learning (ML) models, are provided to illustrate the utility of PySmash in safety profile evaluation, therapeutic activity exploration and molecular optimization, respectively. Its executable program and Python package are available at https://github.com/kotori-y/pySmash.

ChemFLuo: a web-server for structure analysis and identification of fluorescent compounds.

BACKGROUND: Fluorescent detection methods are indispensable tools for chemical biology. However, the frequent appearance of potential fluorescent compound has greatly interfered with the recognition of compounds with genuine activity. Such fluorescence interference is especially difficult to identify as it is reproducible and possesses concentration-dependent characteristic. Therefore, the development of a credible screening tool to detect fluorescent compounds from chemical libraries is urgently needed in early stages of drug discovery. RESULTS: In this study, we developed a webserver ChemFLuo for fluorescent compound detection, based on two large and high-quality training datasets containing 4906 blue and 8632 green fluorescent compounds. These molecules were used to construct a group of prediction models based on the combination of three machine learning algorithms and seven types of molecular representations. The best blue fluorescence prediction model achieved with balanced accuracy (BA) = 0.858 and area under the receiver operating characteristic curve (AUC) = 0.931 for the validation set, and BA = 0.823 and AUC = 0.903 for the test set. The best green fluorescence prediction model achieved the prediction accuracy with BA = 0.810 and AUC = 0.887 for the validation set, and BA = 0.771 and AUC = 0.852 for the test set. Besides prediction model, 22 blue and 16 green representative fluorescent substructures were summarized for the screening of potential fluorescent compounds. The comparison with other fluorescence detection tools and theapplication to external validation sets and large molecule libraries have demonstrated the reliability of prediction model for fluorescent compound detection. CONCLUSION: ChemFLuo is a public webserver to filter out compounds with undesirable fluorescent properties, which will benefit the design of high-quality chemical libraries for drug discovery. It is freely available at http://admet.scbdd.com/chemfluo/index/.

Generation of high-speed PAM-4 signal with 3-bit DAC enabled by CRD-NS in optical interconnect.

In this paper, we experimentally demonstrated a 2-km high-speed optical interconnection with pulse-shaped pre-equalized four-level pulse amplitude modulation (PAM-4) signal generated by a 3-bit digital-to-analog converter (DAC) with the aid of in-band quantization noise suppression techniques under different oversampling ratios (OSRs) to reduce the influence of quantization noise. The simulation results show that the quantization noise suppression capability of high computational complexity digital resolution enhancer (DRE) is sensitive to taps number of the estimated channel and match filter (MF) response when OSR is sufficient, which will lead to further significant computational complexity increase. To better accommodate this issue, channel response-dependent noise shaping (CRD-NS) which also takes channel response into consideration when optimizing quantization noise distribution is proposed to suppress the in-band quantization noise instead of DRE. Experimental results show that about 2 dB receiver sensitivity improvement can be achieved at the hard-decision forward error correction (HD-FEC) threshold for 110 Gb/s pre-equalized PAM-4 signal generated by 3-bit DAC when the traditional NS technique is replaced by the CRD-NS technique. Compared to the high computational complexity DRE technique, in which channel response is also considered, negligible receiver sensitivity penalty is observed for 110 Gb/s PAM-4 signal, when the CRD-NS technique is utilized. Considering both the system cost and bit error ratio (BER) performance, the generation of high-speed PAM signal with 3-bit DAC enabled by the CRD-NS technique is regarded as a promising scheme for optical interconnection.

BRD4 inhibitor suppresses melanoma metastasis via the SPINK6/EGFR-EphA2 pathway.

BET inhibition or BRD4 depletion is a promising and attractive therapy for metastatic melanoma; however, the mechanism is still unclear. Here, we indicated that BET inhibition suppressed melanoma metastasis both in vitro and in vivo and identified a new mechanism by which BET inhibitors suppress melanoma metastasis by blocking the direct interaction of BRD4 and the SPINK6 enhancer. Moreover, we demonstrated that SPINK6 activated the EGFR/EphA2 complex in melanoma and the downstream ERK1/2 and AKT pathways. Thus, these results identified the SPINK6/EGFR-EphA2 axis as a new oncogenic pathway in melanoma metastasis and support the further development of BRD4 inhibitors for the treatment of metastatic melanoma in the clinic.

The locoregional adiponectin and its synergistic antitumor effect with HIF-1α blockade in TSCC.

Adiponectin (APN) is a kind of endogenous anti-tumor adipocytokine, which exerts its function by binding to its receptors (AdipoR1 and AdipoR2). However, hyperadiponectinemia is found in some pathophysiological processes without significant protective effect, which indicates the existence of APN resistance. Here, we aimed to investigate the locoregional expression of APN in tongue squamous cell carcinoma (TSCC) tissues, and to explore the potential regulatory mechanism of APN resistance under hypoxia. Consequently, we found that the protein expression of APN and AdipoR1, but not AdipoR2, was upregulated in the early stage of TSCC and after hypoxic treatment ex vivo and in vitro. Knockdown of HIF-1α decreased the level of APN and AdipoR1, and simultaneously, HIF-1α was identified as transcriptor of the APN. Intriguingly, a regenerative feedback of HIF-1α was unexpectedly detected after application of recombinant globular APN (gAPN), which most likely contributed to the APN resistance. Furthermore, HIF-1α blockade combined with gAPN has a prominent synergistic antitumor effect, which suggested an effective amelioration in APN resistance. In all, our study revealed the possible mechanism of APN resistance under hypoxia and provides a promising strategy of bi-target treatment with APN and HIF-1α for TSCC therapy.

ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties.

Because undesirable pharmacokinetics and toxicity of candidate compounds are the main reasons for the failure of drug development, it has been widely recognized that absorption, distribution, metabolism, excretion and toxicity (ADMET) should be evaluated as early as possible. In silico ADMET evaluation models have been developed as an additional tool to assist medicinal chemists in the design and optimization of leads. Here, we announced the release of ADMETlab 2.0, a completely redesigned version of the widely used AMDETlab web server for the predictions of pharmacokinetics and toxicity properties of chemicals, of which the supported ADMET-related endpoints are approximately twice the number of the endpoints in the previous version, including 17 physicochemical properties, 13 medicinal chemistry properties, 23 ADME properties, 27 toxicity endpoints and 8 toxicophore rules (751 substructures). A multi-task graph attention framework was employed to develop the robust and accurate models in ADMETlab 2.0. The batch computation module was provided in response to numerous requests from users, and the representation of the results was further optimized. The ADMETlab 2.0 server is freely available, without registration, at https://admetmesh.scbdd.com/.

Cloning and expression of recombinant human superoxide dismutase 1 (hSOD1) in Bacillus subtilis 1012.

OBJECTIVE: We aimed to clone and express the human Cu, Zn superoxide dismutase (hSOD1) in Bacillus subtilis 1012. Also, we investigated the expression level of hSOD1 under different induction conditions. RESULT: As an essential member of the antioxidant defense system in vivo, hSOD1 has become a therapeutic agent against host diseases, such as oxygen toxicity, acute inflammation, and radiation injury. The recombinant hSOD1 was successfully secreted extracellularly into B. subtilis 1012. The expression conditions were optimized, including inoculum size, different media, temperatures, and inducer concentrations. Finally, the highest level of hSOD1 was produced as a soluble form in Super rich medium by 2% inoculum with 0.2 mM of IPTG at 37 °C after the induction for 24 h. Besides, 20 g/L of lactose also displayed the same inductive effect on hSOD1 expression as that of IPTG (0.2 mM). Finally, the specific activity of purified hSOD1 was determined to be 1625 U/mg in the presence of 800 µM of Cu2+ and 20 µM of Zn2+. CONCLUSIONS: We propose that the B. subtilis 1012-hSOD1 strain system has great potential in future industrial applications.

Zebrafish: an efficient vertebrate model for understanding role of gut microbiota.

Gut microbiota plays a critical role in the maintenance of host health. As a low-cost and genetically tractable vertebrate model, zebrafish have been widely used for biological research. Zebrafish and humans share some similarities in intestinal physiology and function, and this allows zebrafish to be a surrogate model for investigating the crosstalk between the gut microbiota and host. Especially, zebrafish have features such as high fecundity, external fertilization, and early optical transparency. These enable the researchers to employ the fish to address questions not easily addressed in other animal models. In this review, we described the intestine structure of zebrafish. Also, we summarized the methods of generating a gnotobiotic zebrafish model, the factors affecting its intestinal flora, and the study progress of gut microbiota functions in zebrafish. Finally, we discussed the limitations and challenges of the zebrafish model for gut microbiota studies. In summary, this review established that zebrafish is an attractive research tool to understand mechanistic insights into host-microbe interaction.

Correlation-based transceiver in-phase/ quadrature skew in-field calibration in dual-polarization coherent optical transmission system.

In this paper, we propose a novel transceiver in-phase/quadrature (I/Q) skew in-field calibration scheme with correlation-based method for the dual-polarization coherent optical system. Simultaneous dual-polarization calibration of transceiver I/Q skews after fiber transmission is experimentally performed. Rx/Tx correlation-based skew estimations (CBSEs) are proposed to accurately estimate the transceiver I/Q skews with dual-polarization OFDM signal. By simulation, the robustness of the Rx/Tx CBSEs is investigated against various transceiver I/Q imbalances and channel impairments including carrier frequency offset (CFO), phase noise (PN), and chromatic dispersion (CD). The simultaneous measurement of large transceiver skews is studied within a range of ±128 ps. The bit error rate (BER) improvement brought by the CBSEs is studied in 80 km single-mode fiber (SMF) transmissions under various Rx/Tx skews. In the experiments, the Rx/Tx skew is measured in the range of 1 to 128 ps w/ and w/o the presence of 5 ps Tx/Rx skew. Simultaneous dual-polarization measurements are performed with the X/Y polarization Tx/Rx skews set to 2.5 ps, 5 ps, 7.5 ps and 10 ps, respectively. The measurement errors are within ±0.2 ps. The 80 km SMF dual-polarization transmission after in-field calibration for inter-data center interconnection (inter-DCI) is implemented, with a data rate of 400 Gb/s for both 16QAM and 32QAM modulation formats.

Modified low-bandwidth sub-Nyquist sampling receiving scheme in an IM/DD OFDM system enabled by improved optical shaping.

In this paper, a modified low-bandwidth sub-Nyquist sampling receiving scheme enabled by optical shaping is investigated in an intensity modulation/direct detection (IM/DD) orthogonal frequency-division multiplexing (OFDM) system, which can reduce the sampling rate and analog bandwidth of an analog-to-digital converter (ADC) at the receiving end. By changing the phase matrix of preprocessing, the modified scheme can distinguish different groups of data only by controlling the delay of the shaping module. In addition, the proposed RF sharing architecture can further reduce the cost and increase the feasibility of the scheme. Based on arcsine digital pre-distortion (DPD) technology, a DPD optical pulse shaping scheme is proposed to achieve better spectrum aliasing in the optical domain. With the help of the DPD shaping, we successfully experimentally demonstrate the 12.5-GHz/44.45-Gbit/s IM/DD OFDM system with low-bandwidth (3.125 GHz) and sub-Nyquist sampling rate (6.25 GSa/s) ADC. The experiment results show that the proposed scheme can not only effectively achieve low-bandwidth reception, but also achieve about 0.4 dB receiver sensitivity improvement compared with the traditional high-bandwidth scheme at BER of 3.8×10-3 after 10.2 km standard single mode fiber transmission, which indicates that the proposed scheme is a promising low-cost candidate to provide large transmission capacity for the next-generation network.